Figure 1.
Molecular structures of drugs.
The inclusion relation and the order relation are two distinct ideas in interval analysis. Convexity and nonconvexity create a significant link with different sorts of inequalities under the inclusion relation. For many classes of convex and nonconvex functions, many works have been devoted to constructing and refining classical inequalities. However, it is generally known that log-convex functions play a significant role in convex theory since they allow us to deduce more precise inequalities than convex functions. Because the idea of log convexity is so important, we used fuzzy order relation (⪯) to establish various discrete Jensen and Schur, and Hermite-Hadamard (H-H) integral inequality for log convex fuzzy interval-valued functions (L-convex F-I-V-Fs). Some nontrivial instances are also offered to bolster our findings. Furthermore, we show that our conclusions include as special instances some of the well-known inequalities for L-convex F-I-V-Fs and their variant forms. Furthermore, we show that our conclusions include as special instances some of the well-known inequalities for L-convex F-I-V-Fs and their variant forms. These results and different approaches may open new directions for fuzzy optimization problems, modeling, and interval-valued functions.
Citation: Muhammad Bilal Khan, Hari Mohan Srivastava, Pshtiwan Othman Mohammed, Kamsing Nonlaopon, Y. S. Hamed. Some new Jensen, Schur and Hermite-Hadamard inequalities for log convex fuzzy interval-valued functions[J]. AIMS Mathematics, 2022, 7(3): 4338-4358. doi: 10.3934/math.2022241
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The inclusion relation and the order relation are two distinct ideas in interval analysis. Convexity and nonconvexity create a significant link with different sorts of inequalities under the inclusion relation. For many classes of convex and nonconvex functions, many works have been devoted to constructing and refining classical inequalities. However, it is generally known that log-convex functions play a significant role in convex theory since they allow us to deduce more precise inequalities than convex functions. Because the idea of log convexity is so important, we used fuzzy order relation (⪯) to establish various discrete Jensen and Schur, and Hermite-Hadamard (H-H) integral inequality for log convex fuzzy interval-valued functions (L-convex F-I-V-Fs). Some nontrivial instances are also offered to bolster our findings. Furthermore, we show that our conclusions include as special instances some of the well-known inequalities for L-convex F-I-V-Fs and their variant forms. Furthermore, we show that our conclusions include as special instances some of the well-known inequalities for L-convex F-I-V-Fs and their variant forms. These results and different approaches may open new directions for fuzzy optimization problems, modeling, and interval-valued functions.
One of the life-threatening diseases world-wide is Malaria caused by the bite of female anopheles’ mosquitoes. There are 5 such parasite species that affect humans of which 2 of these viz., P. falciparum and P. vivax are the greatest threats. These two deadliest parasites are prevalent in African continent and Saharan Africa. It is an acute febrile illness which causes rapid onset of fever with headache and chills only after 10 to 15 days after the mosquito bite. If the symptoms are not treated immediately, it will lead to fatal in just one day [1].
In the year 2020, there was a high risk for nearly half the world population of contracting malaria. Some population groups with high risk include infants, and young children, pregnant women, and people with low immunity.
A considerable number of mortality and complications of morbidity is caused by malaria that has become a major health problem globally. As the resistant parasite strains emerge, the therapeutic options are limited which has led to the large spread of malaria. This can be prevented by potential public health emergency in designing new drugs, providing single dose cures and by introducing some novel mechanism of action. New excipients may be included in the medicine that could be effective in fighting the new variants of the strain. Using the available genomic techniques, advancement of the study of biology of the parasites can be developed for the new therapy.
In recent years, various advanced drug interventions have been revealed. This focusses on the discovery of antimalarial agents using the latest scientific and technological advances. There are many antimalarial targets that include proteins such as proteases, plasmodium sugar, farnesyltransferase inhibitor and DNA replication. World malaria report shows an increase of cases from 2019 to 2020 and an increase of almost 69,000 deaths in 2020 compared to 2019 [2].
To test the molecular properties in a laboratory consumes more time and money. This inconvenience can be avoided using quantitative structure activity relationship models to forecast different properties of various chemical compounds using topological descriptors. Topological indices find significant applications in various areas of mathematical chemistry [3,4] such as isomer discrimination, chirality, molecular complexity, drug design, selection of database, QSAR/QSPR/QSTR studies in the recent years [5].
The main objectives of this study are:
● QSPR analysis of the properties of the topological indices using regression models for the nine anti-malaria drugs.
● Computation of topological indices for the considered drugs.
● Comparison of topological indices with correlation coefficients of few physical properties and statistical parameters.
Identifying drug-like from non-drug-like molecules is very much necessary to reduce the cost and time with failed drug development. There are various approaches that screen chemical databases against biological targets in the progress of new potential leads. QSAR modelling is a significant approach in the discovery of drug that correlates the structure of the molecule with biological activities. There are different methods like 2D (topological indices) and 3D (structure-based) of which 2D requires less calculation time and hence used for the preliminary screening of the drug development. Academia, industry, and research institutions across the globe, widely use 2D approach.
The principal step involves the selection of the right descriptor for various reasons. Only few descriptorsgive a better understanding of the results and their interpretability, can reduce the risk of overfitting from redundant descriptors, and provides speedy and cost-effective models.
Topological index represents translations of molecular structures into structural descriptors that are expressed as numerical indices. These topological indices find their applications in drug design where the QSAR/QSPR/QSTR studies are involved. The QSAR/QSPR methods are based on the assumption that the activity or the property, such as a drug binding to DNA or toxic effect, of a certain chemical compound related to its structure through a certain mathematical algorithm.
For the chemical compound under the study, a series of parameters, called chemical descriptors are computed. Then, an algorithm that provides a quite accurate value, similar to theoretical experimental value is found. The final step is to check if the obtained algorithm is capable of predicting the activity/property values.
QSAR/QSPR/QSTR models are used to predict the association between the molecular structure and its activity/property/toxicity. Over the years many algorithms have been proposed and applied in QSAR/QSPR studies. The model framework includes molecular structure (graph) representation, calculation of molecular descriptors (graph invariants) and multiple linear regression method. The model will be validated through statistical parameters (r and r2). The same approach is employed in this work and the statistical parameters have shown significant results.
Recently, Adnan et. al. made a study on the QSPR analysis of anti tuberculosis drugs in which 15 drugs were considered for the regression analysis of 6 physico chemical properties. Eleven topological indices were computed for which the correlation analysis showed a high positive good correlation with all the 6 physico chemical properties. Shanmukha et.al. studied 21 breast cancer drugs for which QSPR analysis were carried out. Eleven topological indices were studied in the study and linear models for each index were carried out. In 2020, Sigarreta [6,7] provided new tools that obtained a unified way inequalities involving many different topological indices. Joseobtained new optimal bounds on the variable Zagreb indices, the variable sum-connectivity index, the variable geometric-arithmetic index and the variable inverse sum indeg index. Recently, Jose computed new lower and upper optimal bounds for general (exponential) indices of a graph. In the same direction, new inequalities involving some well-known topological indices like the generalized atom-bound connectivity index and the generalized second Zagreb index were shown. Some extremal problems for their corresponding exponential indices were computed.
The numerical representation of the arrangement of a molecule of a compound refers to topological index. Wiener index was one of the first topological indices proposed by Harold Wiener in 1947 which proved that his index correlated well with the boiling points of alkanes. At present, there are various indices introduced by various researchers which are divided into three categories, namely, degree-based, neighborhood degree-based and distance based topological indices [8,9,10]. Sixty years ago, QSAR was introduced that included modelling of biological activities of molecules which was extended to modelling of few physicochemical properties of molecules leading to QSPR studies. Initially, it was chemical interpretation of topological indices [11,12] which has now extended to modelling of structural characteristics of molecules with their biological activity leading to complex modelling of compounds in QSAR/QSPR/QSTR studies [13,14,15,16,17,18,19,20,21,22,23,24,25].
This model does not require any lab equipment to perform the analysis. It saves a lot of time and money and the results obtained using the QSPR model are compared with the actual values for further analysis.
A graph G(V, E) with vertex set V(G) and edge set E(G) is connected if there exists an edge between every pair of vertices in G.
The graphs used in this work are simple graphs and have no loops and multiple edges. The number of edges incident to vertex v is the degree of the vertex v, denoted by d_v. For graph terminologies and notations refer [26,27].
Some of the topological descriptors which are used in this work are given below.
The earliest set of topological indices are the first and the second version of Zagreb indices. They have been found impressive in finding the total π-electron energy of molecules. Gutman and Polansky [28] introduced these indices in the year 1986 and are defined as follows
| M1(G)=∑vw∈E(G)(dv+dw) | (1) |
| M2(G)=∑vw∈E(G)(dv×dw) | (2) |
Harmonic index is proposed and defined by Fatjlowicz [29] as
| H(G)=∑vw∈E(G)2(dv+dw) | (3) |
Forgotten index was first defined by Furtula and Gutman [30] in 2015. It became popular as its performance in prediction of the index analogues to that of the original Zagreb index and is defined as
| F(G)=∑vw∈E(G)(dv)2+(dw)2 | (4) |
A novel graph invariant called the SS index of a graph is proposed by Zhao et al. [31] and is stated as
| SS(G)=∑vw∈E(G)√dv×dwdv+dw | (5) |
Ranjini et al. [32] introduced redefined version of the second and third Zagreb indices and are defined as
| ReZG2(G)=∑vw∈E(G)dv×dwdv+dw | (6) |
| ReZG3(G)=∑vw∈E(G)(dv×dw)(dv+dw) | (7) |
In this study, anti-malaria drugs are modelled by simple graphs. To compute the TIs of the considered drug's structure, the employed methods are vertex partitioning, edge partitioning, neighbourhood vertex partitioning and computational techniques. For depicting the results in terms of graphs and verification of computed results, the work is equipped with SPSS17 version.
In this work, degree-based topological indices are computed for the drugs used in the treatment of malaria. The QSPR analysis of the computed indices are discussed, and it is observed that these indices are highly correlated with the physico chemical properties of the drugs that are used in the treatment of malaria.
The nine drugs viz., Chloroquine, Amodiaquine, Mefloquine, Piperaquine, Primaquine, Lumefrantrine, Atovaquone, Pyrimethamine and Doxycycline used in the malaria treatment are considered for the analysis. The molecular structures of these drugs are shown in Figure 1. It is modelled as a graph where the atoms are referred to as vertices while the bonds by its edges which is represented in Figure 2.
The six physical properties of the drugs viz., boiling point, enthalpy, flash point, molar refraction, molar volume and polarizability for the 9 drugs are studied. A linear regression model used in this work is given below.
| P=A+B[TI] | (8) |
where P→ Physical property of the drug, A,B→ constants, TI→ topological descriptor.
Using the linear regression equation discussed, the regression model for the above considered topological indices are defined.
The physical properties of anti-malaria drugs are considered as dependent variables and the topological indices for molecular graphs of 9 drugs are considered as independent variables. A linear regression model is fitted using SPSS software where the constants A and B in the regression equation (8) are calculated by using the training set in Tables 1 and 2.
| Drugs | BP (℃ at 760 mmHg) | Enthalpy (kj/mol) | Flash point (℃) | Molar refraction | Molar volume (cm3) | Polarizability |
| Chloroquine | 460.6 | 72.1 | 232.23 | 97.4 | 287.9 | 38.6 |
| Amodiaquine | 478 | 77 | 242.9 | 105.5 | 282.8 | 41.8 |
| Mefloquine | 415.7 | 70.5 | 205.2 | 83 | 273.4 | 32.9 |
| Piperoquine | 721.1 | 105.3 | 389.9 | 153.7 | 414.2 | 60.9 |
| Primaquine | 451.1 | 71 | 226.6 | 80.5 | 230.3 | 31.9 |
| Lumefrantrine | 642.5 | 99.6 | 342.3 | 151 | 422.3 | 59.9 |
| Atovaquine | 535 | 85.4 | 277.3 | 99.5 | 271.8 | 39.5 |
| Pyrimethamine | 368.4 | 61.5 | 176.6 | 67.1 | 180.2 | 26.6 |
| Doxycycline | 762.6 | 116.5 | 415 | 109 | 271.1 | 43.2 |
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| Drugs | M1(G) | M2(G) | H(G) | F(G) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 106 | 120 | 10.3 | 262 | 23.9599 | 25.2333 | 584 |
| Amodiaquine | 128 | 149 | 11.7333 | 324 | 28.5664 | 30.5333 | 742 |
| Mefloquine | 114 | 137 | 9.5857 | 306 | 24.5829 | 26.631 | 728 |
| Piperoquine | 202 | 240 | 17.8 | 510 | 45.1804 | 48.9 | 1200 |
| Primaquine | 92 | 105 | 9 | 226 | 20.903 | 22.0833 | 512 |
| Lumefrantrine | 182 | 213 | 16.3666 | 466 | 40.3883 | 43.4333 | 1090 |
| Atovaquine | 158 | 194 | 12.6047 | 446 | 33.4116 | 36.5619 | 1064 |
| Pyrimethamine | 86 | 100 | 7.7667 | 222 | 18.9478 | 20.2167 | 506 |
| Doxycycline | 200 | 264 | 14.4164 | 608 | 40.4521 | 45.55 | 1594 |
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Using the linear regression equation discussed, the regression model for the above considered topological indices are defined.
| Boilingpoint=124.096+2.932[M1(G)] |
| Enthalpy=49.292+0.228[M1(G)] |
| Flashpoint=28.813+1.773[M1(G)] |
| MolarRefraction=28.194+0.546[M1(G)] |
| MolarVolume=114.389+1.265[M1(G)] |
| Polarizability=11.164+0.217[M1(G)] |
| Boilingpoint=161.996+2.219[M2(G)] |
| Enthalpy=33.198+0.302[M2(G)] |
| Flashpoint=51.729+1.342[M2(G)] |
| MolarRefraction=41.872+0.374[M2(G)] |
| MolarVolume=150.949+0.838[M2(G)] |
| Polarizability=16.589+0.148[M2(G)] |
| Boilingpoint=87.905+36.905[H(G)] |
| Enthalpy=25.031+4.870[H(G)] |
| Flashpoint=6.939+22.319[H(G)] |
| MolarRefraction=3.348+8.365[H(G)] |
| MolarVolume=44.044+20.421[H(G)] |
| Polarizability=1.318+3.317[H(G)] |
| Boilingpoint=177.821+0.960[F(G)] |
| Enthalpy=34.961+0.132[F(G)] |
| Flashpoint=61.301+0.581[F(G)] |
| MolarRefraction=48.115+0.152[F(G)] |
| MolarVolume=167.744+0.334[F(G)] |
| Polarizability=19.061+0.060[F(G)] |
| Boilingpoint=111.354+13.867[SS(G)] |
| Enthalpy=27.343+1.855[SS(G)] |
| Flashpoint=21.110+8.387[SS(G)] |
| MolarRefraction=18.858+2.811[SS(G)] |
| MolarVolume=87.819+6.670[SS(G)] |
| Polarizability=7.465+1.115[SS(G)] |
| Boilingpoint=123.738+12.440[RezG2(G)] |
| Enthalpy=28.782+1.671[RezG2(G)] |
| Flashpoint=28.597+7.524[RezG2(G)] |
| MolarRefraction=24.545+2.426[RezG2(G)] |
| MolarVolume=103.449+5.693[RezG2(G)] |
| Polarizability=9.720+0.962[RezG2(G)] |
| Boilingpoint=223.151+0.352[RezG3(G)] |
| Enthalpy=40.942+0.049[RezG3(G)] |
| Flashpoint=88.713+0.213[RezG3(G)] |
| MolarRefraction=59.754+0.051[RezG3(G)] |
| MolarVolume=196.894+0.107[RezG3(G)] |
| Polarizability=23.678+0.020[RezG3(G)] |
In Table 1, six physical properties of the anti-malaria drugs considered in the study are presented. Various degree-based topological indices mentioned above are computed and tabulated in Table 2. The correlation of the indices in this study against all the six physical properties are tabulated in Table 3. It is observed that the indices and their corresponding physical properties are highly correlated for most of the properties and the correlation coefficient of second Zagreb index with enthalpy being the highest value of correlation (0.978). The correlation coefficients of the discussed topological indices with physical properties and graphically presented in Figure 3. The statistical parameters viz., the sample size (N), constant (A), slope (b), correlation coefficient (r), the percentage of the dependent variable r2, and p-value for the QSPR study for all the above seven topological indices are studied. The null hypothesis is tested for p-value of each term is where the coefficient equals zero while the higher the p-value infers those changes in predictor are not related to changes in response. In this case, all the regression coefficients of a null hypothesis are zero while testing gives rise to F value. In such a case, the model does not have predictive capability. Using this test, one can compare their model with zero predictor variables to decide their coefficients improve the model.
| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 0.961 | 0.968 | 0.961 | 0.838 | 0.735 | 0.838 |
| M2(G) | 0.962 | 0.978 | 0.963 | 0.76 | 0.645 | 0.76 |
| H(G) | 0.908 | 0.894 | 0.908 | 0.963 | 0.891 | 0.964 |
| F(G) | 0.947 | 0.969 | 0.947 | 0.704 | 0.584 | 0.704 |
| SS(G) | 0.948 | 0.945 | 0.948 | 0.899 | 0.809 | 0.899 |
| ReZG2(G) | 0.957 | 0.958 | 0.957 | 0.873 | 0.776 | 0.873 |
| ReZG3(G) | 0.934 | 0.962 | 0.934 | 0.632 | 0.505 | 0.633 |
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Tables 4–10 represent the statistical parameters such as number of drugs considered, constant, regression coefficient, correlation coefficient, Fisher’s statistic, significant value, and standard error denoted by N, A, b, r, r2, F and p respectively, for all the considered topological indices and physical properties. Table 11 denotes the standard error of estimate for physical properties of drugs. Tables 12–17 denote the comparison of actual and computed values of all physical properties of anti-malaria drugs. Figure 3 depicts the graphical representation of physical properties and the topological indices.
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 124.096 | 2.932 | 0.961 | 0.923 | 83.956 | 0 | Significant |
| EN | 9 | 28.483 | 0.396 | 0.968 | 0.937 | 104.502 | 0 | Significant |
| FP | 9 | 29.813 | 1.773 | 0.961 | 0.923 | 83.973 | 0 | Significant |
| MR | 9 | 28.194 | 0.546 | 0.838 | 0.702 | 16.501 | 0.05 | Significant |
| MV | 9 | 114.389 | 1.265 | 0.735 | 0.541 | 8.244 | 0.02 | Significant |
| PO | 9 | 11.164 | 0.217 | 0.838 | 0.702 | 16.529 | 0 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 161.996 | 2.219 | 0.962 | 0.926 | 88.088 | 0 | Significant |
| EN | 9 | 33.198 | 0.302 | 0.978 | 0.956 | 151.468 | 0 | Significant |
| FP | 9 | 51.729 | 1.342 | 0.963 | 0.926 | 88.162 | 0 | Significant |
| MR | 9 | 41.872 | 0.374 | 0.76 | 0.578 | 9.575 | 0.01 | Significant |
| MV | 9 | 150.949 | 0.838 | 0.645 | 0.416 | 4.982 | 0.06 | Significant |
| PO | 9 | 16.589 | 0.148 | 0.76 | 0.578 | 9.588 | 0.01 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 87.905 | 36.905 | 0.908 | 0.825 | 33.003 | 0.01 | Significant |
| EN | 9 | 25.031 | 4.87 | 0.894 | 0.798 | 27.731 | 0 | Significant |
| FP | 9 | 60939 | 22.319 | 0.908 | 0.825 | 32.986 | 0 | Significant |
| MR | 9 | 3.348 | 8.365 | 0.963 | 0.928 | 90.501 | 0 | Significant |
| MV | 9 | 44.044 | 20.421 | 0.891 | 0.795 | 27.106 | 0 | Significant |
| PO | 9 | 1.318 | 3.317 | 0.964 | 0.928 | 90.704 | 0 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 177.821 | 0.96 | 0.947 | 0.897 | 60.643 | 0 | Significant |
| EN | 9 | 34.961 | 0.132 | 0.969 | 0.94 | 109.495 | 0 | Significant |
| FP | 9 | 61.301 | 0.581 | 0.947 | 0.897 | 60.678 | 0 | Significant |
| MR | 9 | 48.115 | 0.152 | 0.704 | 0.495 | 6.865 | 0.03 | Significant |
| MV | 9 | 167.744 | 0.334 | 0.584 | 0.341 | 3.619 | 0.09 | Significant |
| PO | 9 | 19.061 | 0.06 | 0.704 | 0.496 | 6.877 | 0.03 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 111.354 | 13.867 | 0.948 | 0.898 | 61.748 | 0 | Significant |
| EN | 9 | 27.343 | 1.855 | 0.945 | 0.894 | 58.818 | 0 | Significant |
| FP | 9 | 21.11 | 8.387 | 0.948 | 0.898 | 61.739 | 0 | Significant |
| MR | 9 | 18.858 | 2.811 | 0.899 | 0.808 | 29.522 | 0 | Significant |
| MV | 9 | 87.819 | 6.67 | 0.809 | 0.654 | 13.222 | 0 | Significant |
| PO | 9 | 7.465 | 1.115 | 0.899 | 0.809 | 29.572 | 0 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 123.738 | 12.44 | 0.957 | 0.915 | 75.323 | 0 | Significant |
| EN | 9 | 28.782 | 1.671 | 0.958 | 0.918 | 77.887 | 0 | Significant |
| FP | 9 | 28.597 | 7.524 | 0.957 | 0.915 | 75.331 | 0 | Significant |
| MR | 9 | 24.545 | 2.426 | 0.873 | 0.762 | 22.437 | 0 | Significant |
| MV | 9 | 103.449 | 5.693 | 0.776 | 0.603 | 10.626 | 0.01 | Significant |
| PO | 9 | 9.72 | 0.962 | 0.873 | 0.762 | 22.47 | 0 | Significant |
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| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 223.151 | 0.352 | 0.934 | 0.873 | 47.933 | 0 | Significant |
| EN | 9 | 40.942 | 0.049 | 0.962 | 0.925 | 86.586 | 0 | Significant |
| FP | 9 | 88.713 | 0.213 | 0.934 | 0.873 | 47.97 | 0 | Significant |
| MR | 9 | 59.754 | 0.051 | 0.632 | 0.4 | 4.665 | 0.05 | Significant |
| MV | 9 | 196.894 | 0.107 | 0.505 | 0.255 | 2.4 | 0.05 | Significant |
| PO | 9 | 23.678 | 0.02 | 0.633 | 0.4 | 4.671 | 0.05 | Significant |
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| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 41.3818 | 5.0133 | 25.0251 | 17.3965 | 56.9863 | 6.8934 |
| M2(G) | 40.4727 | 402053 | 24.4681 | 20.7148 | 64.279 | 8.21 |
| H(G) | 62.3993 | 8.9827 | 32.985 | 8.5408 | 38.0989 | 3.3828 |
| F(G) | 47.9859 | 4.9047 | 29.014 | 22.6488 | 68.2796 | 8.9761 |
| SS(G) | 47.5989 | 6.6252 | 28.7891 | 13.955 | 49.478 | 5.5292 |
| ReZG2(G) | 43.4975 | 5.7457 | 26.3056 | 15.5439 | 52.9971 | 6.1595 |
| ReZG3(G) | 53.2487 | 5.4722 | 32.1935 | 24.6926 | 72.5723 | 9.7875 |
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| Drug | Boiling point ℃ (at 760 mmHg) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 460.6 ± 40.0 | 434.88 | 428.27 | 468.02 | 429.341 | 443.60 | 437.64 | 428.71 |
| Amodiaquine | 478.0 ± 45.0 | 499.39 | 492.62 | 520.92 | 488.861 | 507.48 | 503.57 | 484.33 |
| Mefloquine | 415.7 ± 40.0 | 458.34 | 465.99 | 441.66 | 471.581 | 452.24 | 455.02 | 479.40 |
| Piperoquine | 721.1 ± 60.0 | 716.36 | 694.55 | 744.81 | 667.421 | 737.87 | 732.05 | 645.55 |
| Primaquine | 451.1 ± 45.0 | 393.84 | 394.99 | 420.05 | 394.781 | 401.21 | 398.45 | 403.37 |
| Lumefrantrine | 642.5 ± 55.0 | 657.72 | 634.64 | 691.91 | 625.181 | 671.41 | 664.04 | 606.83 |
| Atovaquine | 535.0 ± 50.0 | 587.35 | 592.48 | 553.08 | 605.981 | 574.67 | 578.56 | 597.67 |
| Pyrimethamine | 368.4 ± 52.0 | 376.24 | 383.89 | 374.53 | 390.941 | 374.10 | 375.23 | 401.26 |
| Doxycycline | 762.6 ± 60.0 | 710.49 | 747.81 | 619.94 | 761.501 | 672.30 | 690.38 | 784.23 |
DownLoad:
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| Drug | Flash point (℃) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 232.23 ± 27.3 | 216.75 | 212.76 | 236.82 | 213.52 | 222.06 | 218.45 | 213.10 |
| Amodiaquine | 242.9 ± 28.7 | 255.75 | 251.68 | 268.81 | 249.54 | 260.69 | 258.32 | 246.75 |
| Mefloquine | 205.2 ± 27.3 | 230.93 | 235.58 | 220.88 | 239.08 | 227.28 | 228.96 | 243.77 |
| Piperoquine | 389.9 ± 32.9 | 386.95 | 373.80 | 404.21 | 357.61 | 400.03 | 396.52 | 344.31 |
| Primaquine | 226.6 ± 28.7 | 191.92 | 192.63 | 207.81 | 192.60 | 196.42 | 194.75 | 197.76 |
| Lumefrantrine | 342.3 ± 31.5 | 351.49 | 337.57 | 372.22 | 332.04 | 359.84 | 355.38 | 320.88 |
| Atovaquine | 277.3 ± 30.1 | 308.94 | 312.07 | 288.26 | 320.42 | 301.33 | 303.68 | 315.34 |
| Pyrimethamine | 176.6 ± 30.7 | 181.29 | 185.92 | 180.28 | 190.28 | 180.02 | 180.70 | 196.49 |
| Doxycycline | 415.0 ± 32.9 | 383.41 | 406.01 | 328.69 | 414.54 | 360.38 | 371.31 | 428.23 |
DownLoad:
CSV
| Drug | Enthalpy (kj/mol) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 72.1 ± 3.0 | 73.46 | 69.438 | 75.19 | 69.545 | 71.78 | 70.94 | 69.55 |
| Amodiaquine | 77.0 ± 3.0 | 78.476 | 78.19 | 82.17 | 77.72 | 80.33 | 79.80 | 77.3 |
| Mefloquine | 70.5 ± 3.0 | 75.28 | 74.57 | 71.71 | 75.35 | 72.94 | 73.28 | 76.61 |
| Piperoquine | 105.3 ± 3.0 | 95.34 | 105.67 | 111.71 | 102.28 | 111.15 | 110.49 | 99.74 |
| Primaquine | 71.0 ± 3.0 | 70.268 | 64.90 | 68.86 | 64.79 | 66.11 | 65.68 | 66.03 |
| Lumefrantrine | 99.6 ± 3.0 | 90.788 | 97.52 | 104.73 | 96.47 | 102.26 | 101.35 | 94.35 |
| Atovaquine | 85.4 ± 3.0 | 85.316 | 91.78 | 86.41 | 93.83 | 89.31 | 89.87 | 93.07 |
| Pyrimethamine | 61.5 ± 3.0 | 68.9 | 63.39 | 62.85 | 64.26 | 62.48 | 62.56 | 65.73 |
| Doxycycline | 116.5 ± 3.0 | 94.892 | 112.92 | 95.23 | 115.21 | 102.37 | 104.89 | 119.04 |
DownLoad:
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| Drug | Molar refraction (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 97.4 ± 0.3 | 86.07 | 86.75 | 89.50 | 87.93 | 86.20 | 85.76 | 89.53 |
| Amodiaquine | 105.5 ± 0.3 | 98.08 | 97.59 | 101.49 | 97.36 | 99.15 | 98.61 | 97.59 |
| Mefloquine | 83.0 ± 0.3 | 90.43 | 93.11 | 83.53 | 94.62 | 87.96 | 89.15 | 96.88 |
| Piperoquine | 153.7 ± 0.3 | 138.48 | 131.63 | 152.24 | 125.63 | 145.86 | 143.17 | 120.95 |
| Primaquine | 80.5 ± 0.3 | 78.42 | 81.14 | 78.63 | 82.46 | 77.61 | 78.11 | 85.86 |
| Lumefrantrine | 151.0 ± 0.3 | 127.56 | 121.53 | 140.25 | 118.94 | 132.38 | 129.91 | 115.34 |
| Atovaquine | 99.5 ± 0.3 | 114.46 | 114.42 | 108.78 | 115.90 | 112.77 | 113.24 | 114.01 |
| Pyrimethamine | 67.1 ± 0.5 | 75.15 | 79.27 | 68.31 | 81.85 | 72.12 | 73.59 | 85.56 |
| Doxycycline | 109 ± 0.4 | 137.39 | 140.60 | 123.94 | 140.53 | 132.56 | 135.04 | 141.04 |
DownLoad:
CSV
| Drug | Molar volume (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZzG3(G) |
| Chloroquine | 287.9 ± 3.0 | 248.47 | 251.509 | 254.38 | 255.25 | 247.63 | 247.10 | 259.38 |
| Amodiaquine | 282.8 ± 3.0 | 276.30 | 275.811 | 283.64 | 275.96 | 278.35 | 277.27 | 276.28 |
| Mefloquine | 273.4 ± 3.0 | 258.59 | 265.755 | 239.79 | 269.94 | 251.78 | 255.05 | 274.79 |
| Piperoquine | 414.2 ± 3.0 | 369.91 | 352.069 | 407.53 | 338.08 | 389.17 | 381.83 | 325.29 |
| Primaquine | 230.3 ± 3.0 | 230.76 | 238.939 | 227.83 | 243.22 | 227.24 | 229.16 | 251.67 |
| Lumefrantrine | 422.3 ± 3.0 | 344.61 | 329.443 | 378.26 | 323.38 | 357.20 | 350.71 | 313.52 |
| Atovaquine | 271.8 ± 3.0 | 314.25 | 313.521 | 301.44 | 316.70 | 310.67 | 311.59 | 310.74 |
| Pyrimethamine | 180.2 ± 7.0 | 223.17 | 234.749 | 202.64 | 241.89 | 214.20 | 218.54 | 251.03 |
| Doxycycline | 271.1 ± 5.0 | 367.38 | 372.181 | 338.44 | 370.81 | 357.63 | 362.76 | 367.45 |
DownLoad:
CSV
| Drug | Polarizability (10-24 cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 38.6 ± 0.5 | 34.16 | 34.34 | 35.48 | 34.78 | 34.18 | 33.99 | 35.35 |
| Amodiaquine | 41.8 ± 0.5 | 38.94 | 38.64 | 40.23 | 38.50 | 39.31 | 39.09 | 38.51 |
| Mefloquine | 32.9 ± 0.5 | 35.90 | 36.86 | 33.11 | 37.42 | 34.87 | 35.33 | 38.23 |
| Piperoquine | 60.9 ± 0.5 | 54.99 | 52.10 | 60.36 | 49.66 | 57.84 | 56.76 | 47.67 |
| Primaquine | 31.9 ± 0.5 | 31.12 | 32.12 | 31.17 | 32.62 | 30.77 | 30.96 | 33.91 |
| Lumefrantrine | 59.9 ± 0.5 | 50.65 | 48.11 | 55.60 | 47.02 | 52.49 | 51.50 | 45.47 |
| Atovaquine | 39.5 ± 0.5 | 45.45 | 45.30 | 43.12 | 45.82 | 44.71 | 44.89 | 44.95 |
| Pyrimethamine | 26.6 ± 0.5 | 29.82 | 31.38 | 27.08 | 32.38 | 28.59 | 29.16 | 33.79 |
| Doxycycline | 43.2 ± 0.5 | 54.56 | 55.66 | 49.13 | 55.54 | 52.56 | 53.53 | 55.55 |
DownLoad:
CSV
In this study, seven degree-based topological indices are computed for nine anti-malaria drugs which is presented in Table 2. The coefficient of correlation for the said indices against six physical properties are computed and tabulated in Table 3. It is observed from Table 3 that every topological index shows good correlation with its corresponding physical properties in which, considering second Zagreb index row wise, the correlation coefficient is high with that of enthalpy for M2(G). The first Zagreb index has a high correlation with enthalpy of 0.963. The Harmonic index shows good correlation with polarizability, Forgotten index with enthalpy having r = 0.969, SS index has highest correlation with boiling point and flash point, both being 0.948, while the redefined Zagreb indices with enthalpy having 0.947, 0.958 and 0.962 respectively for the three indices.
This work focusses on the drugs used for the treatment of malaria which is caused by mosquitoes. The analysis done in this paper may help the chemists and pharmaceutical industry researchers to design novel drugs using various excipients by the values of indices computed here. They may constitute different drugs for various ailments based on the available topological indices in the article. The correlation coefficients of various drugs help the chemists to choose the right composition based on the high correlation value to form a new drug for novel ailments. New tools to obtain a unified way of involving inequalities of various topological indices may be carried out. The extremal values for the considered indices may be computed for exponential version of the indices.
The author declare that they have no conflict of interest.
| [1] |
G. D. Anderson, M. K. Vamanamurthy, M. Vuorinen, Generalized convexity and inequalities, J. Math. Anal. Appl., 335 (2007), 1294-1308. https://doi.org/10.1016/j.jmaa.2007.02.016 doi: 10.1016/j.jmaa.2007.02.016
|
| [2] |
M. Avci, H. Kavurmaci, M. E. Özdemir, New inequalities of Hermite-Hadamard type via s-convex functions in the second sense with applications, Appl. Math. Comput., 217 (2011), 5171-5176. https://doi.org/10.1016/j.amc.2010.11.047 doi: 10.1016/j.amc.2010.11.047
|
| [3] | F. Chen, S. Wu, Integral inequalities of Hermite-Hadamard type for products of two h-convex functions, Abstr. Appl. Anal., 6 (2014), 1-6. |
| [4] |
W. Liu, New integral inequalities involving beta function via P-convexity, Miskolc Math. Notes, 15 (2014), 585-591. https://doi.org/10.18514/MMN.2014.660 doi: 10.18514/MMN.2014.660
|
| [5] | J. Hadamard, É tude sur les propriétés des fonctions entières et en particulier d'une fonction considérée par Riemann, J. Math. Pure. Appl., 5 (1893), 171-216. |
| [6] | C. Hermite, Sur deux limites d'une intégrale définie, Mathesis, 3 (1883), 1-82. |
| [7] |
M. Alomari, M. Darus, S. S. Dragomir, P. Cerone, Ostrowski type inequalities for functions whose derivatives are s-convex in the second sense, Appl. Math. Lett., 23 (2010), 1071-1076. https://doi.org/10.1016/j.aml.2010.04.038 doi: 10.1016/j.aml.2010.04.038
|
| [8] |
P. Cerone, S. S. Dragomir, Ostrowski type inequalities for functions whose derivatives satisfy certain convexity assumptions, Demonstr. Math., 37 (2004), 299-308. https://doi.org/10.1515/dema-2004-0208 doi: 10.1515/dema-2004-0208
|
| [9] |
M. A. Latif, S. Rashid, S. S. Dragomir, Y. M. Chu, Hermite-Hadamard type inequalities for co-ordinated convex and quasi-convex functions and their applications, J. Inequal. Appl., 2019 (2019), 317. https://doi.org/10.1186/s13660-019-2272-7 doi: 10.1186/s13660-019-2272-7
|
| [10] | L. Fejér, Uber die fourierreihen, Ⅱ, Math. Naturwise. Anz, Ungar. Akad. Wiss., 24 (1906), 369-390. |
| [11] |
S. Varošanec, On h-convexity, J. Math. Anal. Appl., 326 (2007), 303-311. https://doi.org/10.1016/j.jmaa.2006.02.086 doi: 10.1016/j.jmaa.2006.02.086
|
| [12] |
B. Y. Xi, F. Qi, Some integral inequalities of Hermite-Hadamard type for s-log convex functions, Acta Math. Sci. Ser. A (Chin. Ed.), 35 (2015), 515-524. https://doi.org/10.13140/RG.2.1.4385.9044 doi: 10.13140/RG.2.1.4385.9044
|
| [13] |
M. A. Noor, F. Qi, M. U. Awan, Some Hermite-Hadamard type inequalities for log-h-convex functions, Analysis, 33 (2013), 367-375. https://doi.org/10.1524/anly.2013.1223 doi: 10.1524/anly.2013.1223
|
| [14] | M. A. Noor, Hermite-Hadamard integral inequalities for log-preinvex functions, J. Math. Anal. Approx. Theory, 2 (2007), 126-131. |
| [15] | J. E. Peajcariaac, F. Proschan, Y. L. Tong, Convex functions, partial orderings and statistical applications, Academic Press, 1992. |
| [16] | S. S. Dragomir C. E. M. Pearce, Selected topics on Hermite-Hadamard inequalities and applications, 2003. |
| [17] | S. S. Dragomir, Refinements of the Hermite-Hadamard integral inequality for log-convex functions, 2000. |
| [18] |
S. S. Dragomir, B. Mond, Integral inequalities of Hadamard type for log convex functions, Demonstr. Math., 31 (1998), 354-364. https://doi.org/10.1515/dema-1998-0214 doi: 10.1515/dema-1998-0214
|
| [19] |
S. S. Dragomir, New inequalities of Hermite-Hadamard type for log convex functions, Khayyam J. Math., 3 (2017), 98-115. https://doi.org/10.22034/kjm.2017.47458 doi: 10.22034/kjm.2017.47458
|
| [20] | S. S. Dragomir, J. Pecaric, L. E. Persson, Some inequalities of Hadamard type, Soochow J. Math., 21 (1995), 335-341. |
| [21] | S. S. Dragomir, A survey of Jensen type inequalities for log-convex functions of self adjoint operators in Hilbert spaces, Commun. Math. Anal., 10 (2011), 82-104. |
| [22] |
Jr R. Goetschel, W. Voxman, Elementary fuzzy calculus, Fuzzy set. syst., 18 (1986), 31-43. https://doi.org/10.1016/0165-0114(86)90026-6 doi: 10.1016/0165-0114(86)90026-6
|
| [23] | R. E. Moore, Interval analysis, Englewood Cliffs: Prentice Hall, 1966. |
| [24] | U. W. Kulish, W. Miranker, Computer arithmetic in theory and practice, New York: Academic Press, 1981. |
| [25] |
S. Nanda, K. Kar, Convex fuzzy mappings, Fuzzy Set. Syst., 48 (1992), 129-132. https://doi.org/10.1016/0165-0114(92)90256-4 doi: 10.1016/0165-0114(92)90256-4
|
| [26] | S. S. Chang, Variational inequality and complementarity problems theory and applications, Shanghai: Shanghai Scientific and Technological Literature Publishing House, 1991. |
| [27] |
M. A. Noor, Fuzzy preinvex functions, Fuzzy Set. Syst., 64 (1994), 95-104. https://doi.org/10.1016/0165-0114(94)90011-6 doi: 10.1016/0165-0114(94)90011-6
|
| [28] | B. Bede, Mathematics of fuzzy sets and fuzzy logic studies in fuzziness and soft computing, In: Studies in fuzziness and soft computing springer, 2013. https://doi.org/10.1007/978-3-642-35221-8 |
| [29] | J. Cervelati, M. D. Jiménez-Gamero, F. Vilca-Labra, M. A. Rojas-Medar, Continuity for s-convex fuzzy processes, In: Soft methodology and random information systems, 2004,653-660. https://doi.org/10.1007/978-3-540-44465-7_81 |
| [30] |
Y. Chalco-Cano, M. A. Rojas-Medar, H. Román-Flores, M-convex fuzzy mappings and fuzzy integral mean, Comput. Math. Appl., 40 (2000), 1117-1126. https://doi.org/10.1016/S0898-1221(00)00226-1 doi: 10.1016/S0898-1221(00)00226-1
|
| [31] |
M. B. Khan, M. A. Noor, K. I. Noor, A. T. Ab Ghani, L. Abdullah, Extended perturbed mixed variational-like inequalities for fuzzy mappings, J. Math., 2021 (2021), 6652930. https://doi.org/10.1155/2021/6652930 doi: 10.1155/2021/6652930
|
| [32] |
M. L. Puri, D. A. Ralescu, Fuzzy random variables, J. Math. Anal. Appl., 114 (1986), 409-422. https://doi.org/10.1016/0022-247X(86)90093-4 doi: 10.1016/0022-247X(86)90093-4
|
| [33] |
H. Román-Flores, Y. Chalco-Cano, W. A. Lodwick, Some integral inequalities for interval-valued functions, Comput. Appl. Math., 37 (2018), 1306-1318. https://doi.org/10.1007/s40314-016-0396-7 doi: 10.1007/s40314-016-0396-7
|
| [34] | H. Roman-Flores, Y. Chalco-Cano, G. N. Silva, A note on Gronwall type inequality for interval-valued functions, In: 2013 IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, 1455-1458. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608616 |
| [35] |
Y. Chalco-Cano, A. Flores-Franulic, H. Román-Flores, Ostrowski type inequalities for interval-valued functions using generalized Hukuhara derivative, Comput. Appl. Math., 31 (2012), 457-472. https://doi.org/10.1590/S1807-03022012000300002 doi: 10.1590/S1807-03022012000300002
|
| [36] |
Y. Chalco-Cano, W. A. Lodwick, W. Condori-Equice, Ostrowski type inequalities and applications in numerical integration for interval-valued functions, Soft Comput., 19 (2015), 3293-3300. https://doi.org/10.1007/s00500-014-1483-6 doi: 10.1007/s00500-014-1483-6
|
| [37] |
D. Zhang, C. Guo, D. Chen, G. Wang, Jensen's inequalities for set-valued and fuzzy set-valued functions, Fuzzy Set. Syst., 404 (2021), 178-204. https://doi.org/10.1016/j.fss.2020.06.003 doi: 10.1016/j.fss.2020.06.003
|
| [38] |
T. M. Costa, Jensen's inequality type integral for fuzzy-interval-valued functions, Fuzzy Set. Syst., 327 (2017), 31-47. https://doi.org/10.1016/j.fss.2017.02.001 doi: 10.1016/j.fss.2017.02.001
|
| [39] |
T. M. Costa, H. Roman-Flores, Some integral inequalities for fuzzy-interval-valued functions, Inform. Sci., 420 (2017), 110-125. https://doi.org/10.1016/j.ins.2017.08.055 doi: 10.1016/j.ins.2017.08.055
|
| [40] |
O. Kaleva, Fuzzy differential equations, Fuzzy set. Sys., 24 (1987), 301-317. https://doi.org/10.1016/0165-0114(87)90029-7 doi: 10.1016/0165-0114(87)90029-7
|
| [41] |
J. Matkowski, K. Nikodem, An integral Jensen inequality for convex multifunctions, Results Math., 26 (1994), 348-353. https://doi.org/10.1007/BF03323058 doi: 10.1007/BF03323058
|
| [42] | P. Diamond, P. E. Kloeden, Metric spaces of fuzzy sets: Theory and applications, World Scient, 1994. |
| [43] |
M. B. Khan, M. A. Noor, K. I. Noor, Y. M. Chu, Higher-order strongly preinvex fuzzy mappings and fuzzy mixed variational-like inequalities, Int. J. Comput. Intell. Syst., 14 (2021), 186-1870. https://doi.org/10.2991/ijcis.d.210616.001 doi: 10.2991/ijcis.d.210616.001
|
| [44] |
P. Liu, M. B. Khan, M. A. Noor, K. I. Noor, On strongly generalized preinvex fuzzy mappings, J. Math., 2021 (2021), 6657602. https://doi.org/10.1155/2021/6657602 doi: 10.1155/2021/6657602
|
| [45] |
P. Liu, M. B. Khan, M. A. Noor, K. I. Noor, New Hermite-Hadamard and Jensen inequalities for log-s-convex fuzzy-interval-valued functions in the second sense, Complex Intell. Syst., 2021 (2021), 1-15. https://doi.org/10.1007/s40747-021-00379-w doi: 10.1007/s40747-021-00379-w
|
| [46] |
M. B. Khan, M. A. Noor, L. Abdullah, K. I. Noor, New Hermite-Hadamard and Jensen inequalities for log-h-convex fuzzy-interval-valued functions, Int. J. Comput. Intell. Syst., 14 (2021), 1-16. https://doi.org/10.1007/s44196-021-00004-1 doi: 10.1007/s44196-021-00004-1
|
| [47] |
M. B. Khan, M. A. Noor, K. I. Noor, Y. M. Chu, New Hermite-Hadamard type inequalities for (h1, h2)-convex fuzzy-interval-valued functions, Adv. Differ. Equ., 2021 (2021), 6-20. https://doi.org/10.1186/s13662-021-03245-8 doi: 10.1186/s13662-021-03245-8
|
| [48] |
M. B. Khan, M. A. Noor, L. Abdullah, Y. M. Chu, Some new classes of preinvex fuzzy-interval-valued functions and inequalities, Int. J. Comput. Intell. Syst., 14 (2021), 1403-1418. https://doi.org/10.2991/ijcis.d.210409.001 doi: 10.2991/ijcis.d.210409.001
|
| [49] |
H. M. Srivastava, S. M. El-Deeb, Fuzzy differential subordinations based upon the Mittag-Leffler type Borel distribution, Symmetry, 13 (2021), 1-15. https://doi.org/10.3390/sym13061023 doi: 10.3390/sym13061023
|
| [50] |
M. B. Khan, H. M. Srivastava, P. O. Mohammad, J. L. G. Guirao, Fuzzy mixed variational-like and integral inequalities for strongly preinvex fuzzy mappings, Symmetry, 13 (2021), 1816. https://doi.org/10.3390/sym13101816 doi: 10.3390/sym13101816
|
| [51] |
M. B. Khan, P. O. Mohammed, M. A. Noor, Y. S. Hamed, New Hermite-Hadamard inequalities in fuzzy-interval fractional calculus and related inequalities, Symmetry, 13 (2021), 673. https://doi.org/10.3390/sym13040673 doi: 10.3390/sym13040673
|
| [52] |
M. B. Khan, M. A. Noor, H. M. Al-Bayatti, K. I. Noor, Some new inequalities for LR-Log-h-convex interval-valued functions by means of pseudo order relation, Appl. Math. Inform. Sci., 15 (2021), 459-470. https://doi.org/10.18576/amis/150408 doi: 10.18576/amis/150408
|
| [53] |
G. Sana, M. B. Khan, M. A. Noor, P. O. Mohammed, Y. M. Chu, Harmonically convex fuzzy-interval-valued functions and fuzzy-interval Riemann-Liouville fractional integral inequalities, Int. J. Comput. Intell. Syst., 14 (2021), 1809-1822. https://dx.doi.org/10.2991/ijcis.d.210620.001 doi: 10.2991/ijcis.d.210620.001
|
| [54] |
H. M. Srivastava, Z. H. Zhang, Y. D. Wu, Some further refinements and extensions of the Hermite-Hadamard and Jensen inequalities in several variables, Math. Comput. Model., 54 (2021), 2709-2717. https://dx.doi.org/10.1016/j.mcm.2011.06.057 doi: 10.1016/j.mcm.2011.06.057
|
| [55] |
M. B. Khan, M. A. Noor, K. I. Noor, H. Almusawa, K. S. Nisar, Exponentially preinvex fuzzy mappings and fuzzy exponentially mixed variational-like inequalities, Int. J. Nonlinear Anal. Appl., 19 (2021), 518-541. https://dx.doi.org/10.28924/2291-8639-19-2021-518 doi: 10.28924/2291-8639-19-2021-518
|
| [56] |
M. B. Khan, P. O. Mohammed, M. A. Noor, K. Abuahalnaja, Fuzzy integral inequalities on coordinates of convex fuzzy interval-valued functions, Math. Biosci. Eng., 18 (2021), 6552-6580. https://dx.doi.org/10.3934/mbe.2021325 doi: 10.3934/mbe.2021325
|
| [57] |
M. B. Khan, P. O. Mohammed, M. A. Noor, Y. Hameed. K. I. Noor, New fuzzy-interval inequalities in fuzzy-interval fractional calculus by means of fuzzy order relation, AIMS Mathematics, 6 (2021), 10964-10988. https://dx.doi.org/10.3934/math.2021637 doi: 10.3934/math.2021637
|
| [58] |
M. B. Khan, P. O. Mohammed, M. A. Noor, D. Baleanu, J. Guirao, Some new fractional estimates of inequalities for LR-p-convex interval-valued functions by means of pseudo order relation, Axioms, 10 (2021), 175. https://doi.org/10.3390/axioms10030175 doi: 10.3390/axioms10030175
|
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| 5. | Mazhar Hussain, Muhammad Kamran Siddiqui, Muhammad Farhan Hanif, Hasan Mahmood, Zohaib Saddique, Samuel Asefa Fufa, On K-Banhatti indices and entropy measure for rhodium (III) chloride via linear regression models, 2023, 9, 24058440, e20935, 10.1016/j.heliyon.2023.e20935 | |
| 6. | Ali N. A. Koam, Muhammad Faisal Nadeem, Ali Ahmad, Hassan A. Eshaq, Weighted Asymmetry Index: A New Graph-Theoretic Measure for Network Analysis and Optimization, 2024, 12, 2227-7390, 3397, 10.3390/math12213397 | |
| 7. | Muhammad Waheed Rasheed, Abid Mahboob, Iqra Hanif, Uses of degree-based topological indices in QSPR analysis of alkaloids with poisonous and healthful nature, 2024, 12, 2296-424X, 10.3389/fphy.2024.1381887 | |
| 8. | Micheal Arockiaraj, J. Celin Fiona, Arul Jeya Shalini, Comparative Study of Entropies in Silicate and Oxide Frameworks, 2024, 16, 1876-990X, 3205, 10.1007/s12633-024-02892-2 | |
| 9. | Asad Ullah, Safina Jabeen, Shahid Zaman, Anila Hamraz, Summeira Meherban, Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules, 2024, 71, 0009-4536, 250, 10.1002/jccs.202300450 | |
| 10. | Tahreem Ashraf, Nazeran Idrees, Topological indices based VIKOR assisted multi-criteria decision technique for lung disorders, 2024, 12, 2296-2646, 10.3389/fchem.2024.1407911 | |
| 11. | Abid Mahboob, Muhammad Waheed Rasheed, Iqra Hanif, Laiba Amin, Abdu Alameri, Role of molecular descriptors in quantitative structure‐property relationship analysis of kidney cancer therapeutics, 2024, 124, 0020-7608, 10.1002/qua.27241 | |
| 12. | B. Vijayakumar, B. Karthikeyan, A. Nelson, K. Arputha Christy, Synthesis, characterisation and topological indices approach of hydrazone derivatives of disubstituted-2,6-diaryl piperidin-4-one, catalysed by CdO-SnO2 nanocatalyst, 2024, 1306, 00222860, 137843, 10.1016/j.molstruc.2024.137843 | |
| 13. | Abdul Hakeem, Nek Muhammad Katbar, Fazal Muhammad, Nisar Ahmed, On the molecular structure modelling of gamma graphyne and armchair graphyne nanoribbon via reverse degree-based topological indices, 2024, 122, 0026-8976, 10.1080/00268976.2023.2259510 | |
| 14. | Deepa Balasubramaniyan, Natarajan Chidambaram, Vignesh Ravi, Muhammad Kamran Siddiqui, QSPR analysis of anti‐asthmatic drugs using some new distance‐based topological indices: A comparative study, 2024, 124, 0020-7608, 10.1002/qua.27372 | |
| 15. | Ali Raza, Muhammad Mobeen Munir, Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis, 2024, 12, 2296-424X, 10.3389/fphy.2024.1348407 | |
| 16. | Muhammad Waheed Rasheed, Abid Mahboob, Iqra Hanif, On QSAR modeling with novel degree-based indices and thermodynamics properties of eye infection therapeutics, 2024, 12, 2296-2646, 10.3389/fchem.2024.1383206 | |
| 17. | Asma Jabeen, Shahzad Ahmad, Shahid Zaman, The study of regression model based on CoM-polynomial in blood cancer drug properties, 2024, 9, 26668181, 100648, 10.1016/j.padiff.2024.100648 | |
| 18. | Deepa Balasubramaniyan, Natarajan Chidambaram, On some neighbourhood degree-based topological indices with QSPR analysis of asthma drugs, 2023, 138, 2190-5444, 10.1140/epjp/s13360-023-04439-7 | |
| 19. | Abid Mahboob, Muhammad Waheed Rasheed, Aya Mohammed Dhiaa, Iqra Hanif, Laiba Amin, On quantitative structure-property relationship (QSPR) analysis of physicochemical properties and anti-hepatitis prescription drugs using a linear regression model, 2024, 10, 24058440, e25908, 10.1016/j.heliyon.2024.e25908 | |
| 20. | Deepalakshmi Sarkarai, Vignesh Ravi, Kalyani Desikan, 2024, 3180, 0094-243X, 020014, 10.1063/5.0224422 | |
| 21. | Esra Öztürk Sözen, Elif Eryaşar, Şükriye Çakmak, Szeged-like topological descriptors and COM -polynomials for graphs of some Alzheimer’s agents , 2024, 122, 0026-8976, 10.1080/00268976.2024.2305853 | |
| 22. | Xiujun Zhang, Shamaila Yousaf, Anisa Naeem, Ferdous M. Tawfiq, Adnan Aslam, Analyzing topological descriptors of guar gum and its derivatives for predicting physical properties in carbohydrates, 2024, 253, 01697439, 105203, 10.1016/j.chemolab.2024.105203 | |
| 23. | Gayathri K. B, S. Roy, Quantitative structure property relationship study of postpartum depression medications using topological indices and regression models, 2024, 20904479, 103194, 10.1016/j.asej.2024.103194 | |
| 24. | Fatima Saeed, Nazeran Idrees, Integrating Structural Modeling and Decision-Making for Anti-psychotic Drugs through Topological Indices, 2025, 15, 2191-1630, 10.1007/s12668-024-01732-2 | |
| 25. | Deepa Balasubramaniyan, Natarajan Chidambaram, Vignesh Ravi, 2024, Chapter 29, 978-3-031-69145-4, 380, 10.1007/978-3-031-69146-1_29 | |
| 26. | W. Tamilarasi, B. J. Balamurugan, New reverse sum Revan indices for physicochemical and pharmacokinetic properties of anti-filovirus drugs, 2024, 12, 2296-2646, 10.3389/fchem.2024.1486933 | |
| 27. | Muneeba Mansha, Sarfraz Ahmad, Zahid Raza, Eccentric indices based QSPR evaluation of drugs for schizophrenia treatment, 2025, 11, 24058440, e42222, 10.1016/j.heliyon.2025.e42222 | |
| 28. | Micheal Arockiaraj, J. J. Jeni Godlin, S. Radha, Comparative study of degree and neighborhood degree sum-based topological indices for predicting physicochemical properties of skin cancer drug structures, 2025, 0217-9849, 10.1142/S0217984925501064 | |
| 29. | Adnan Asghar, QSPR analysis of anti-hepatitis prescription drugs using degree based topological indices through M-polynomial and NM-polynomial, 2025, 12, 2411-1414, 10.15826/chimtech.2025.12.2.06 | |
| 30. | S. Gayathri, K. Dhanalakshmi, Ebenezer Bonyah, QSPR analysis of vitiligo treatment of some drugs using reverse degree-based topological indices, 2025, 24682276, e02636, 10.1016/j.sciaf.2025.e02636 |
Muhammad Bilal Khan, Hari Mohan Srivastava, Pshtiwan Othman Mohammed, Kamsing Nonlaopon, Y. S. Hamed. Some new Jensen, Schur and Hermite-Hadamard inequalities for log convex fuzzy interval-valued functions[J]. AIMS Mathematics, 2022, 7(3): 4338-4358. doi: 10.3934/math.2022241
| Drugs | BP (℃ at 760 mmHg) | Enthalpy (kj/mol) | Flash point (℃) | Molar refraction | Molar volume (cm3) | Polarizability |
| Chloroquine | 460.6 | 72.1 | 232.23 | 97.4 | 287.9 | 38.6 |
| Amodiaquine | 478 | 77 | 242.9 | 105.5 | 282.8 | 41.8 |
| Mefloquine | 415.7 | 70.5 | 205.2 | 83 | 273.4 | 32.9 |
| Piperoquine | 721.1 | 105.3 | 389.9 | 153.7 | 414.2 | 60.9 |
| Primaquine | 451.1 | 71 | 226.6 | 80.5 | 230.3 | 31.9 |
| Lumefrantrine | 642.5 | 99.6 | 342.3 | 151 | 422.3 | 59.9 |
| Atovaquine | 535 | 85.4 | 277.3 | 99.5 | 271.8 | 39.5 |
| Pyrimethamine | 368.4 | 61.5 | 176.6 | 67.1 | 180.2 | 26.6 |
| Doxycycline | 762.6 | 116.5 | 415 | 109 | 271.1 | 43.2 |
DownLoad:
CSV
| Drugs | M1(G) | M2(G) | H(G) | F(G) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 106 | 120 | 10.3 | 262 | 23.9599 | 25.2333 | 584 |
| Amodiaquine | 128 | 149 | 11.7333 | 324 | 28.5664 | 30.5333 | 742 |
| Mefloquine | 114 | 137 | 9.5857 | 306 | 24.5829 | 26.631 | 728 |
| Piperoquine | 202 | 240 | 17.8 | 510 | 45.1804 | 48.9 | 1200 |
| Primaquine | 92 | 105 | 9 | 226 | 20.903 | 22.0833 | 512 |
| Lumefrantrine | 182 | 213 | 16.3666 | 466 | 40.3883 | 43.4333 | 1090 |
| Atovaquine | 158 | 194 | 12.6047 | 446 | 33.4116 | 36.5619 | 1064 |
| Pyrimethamine | 86 | 100 | 7.7667 | 222 | 18.9478 | 20.2167 | 506 |
| Doxycycline | 200 | 264 | 14.4164 | 608 | 40.4521 | 45.55 | 1594 |
DownLoad:
CSV
| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 0.961 | 0.968 | 0.961 | 0.838 | 0.735 | 0.838 |
| M2(G) | 0.962 | 0.978 | 0.963 | 0.76 | 0.645 | 0.76 |
| H(G) | 0.908 | 0.894 | 0.908 | 0.963 | 0.891 | 0.964 |
| F(G) | 0.947 | 0.969 | 0.947 | 0.704 | 0.584 | 0.704 |
| SS(G) | 0.948 | 0.945 | 0.948 | 0.899 | 0.809 | 0.899 |
| ReZG2(G) | 0.957 | 0.958 | 0.957 | 0.873 | 0.776 | 0.873 |
| ReZG3(G) | 0.934 | 0.962 | 0.934 | 0.632 | 0.505 | 0.633 |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 124.096 | 2.932 | 0.961 | 0.923 | 83.956 | 0 | Significant |
| EN | 9 | 28.483 | 0.396 | 0.968 | 0.937 | 104.502 | 0 | Significant |
| FP | 9 | 29.813 | 1.773 | 0.961 | 0.923 | 83.973 | 0 | Significant |
| MR | 9 | 28.194 | 0.546 | 0.838 | 0.702 | 16.501 | 0.05 | Significant |
| MV | 9 | 114.389 | 1.265 | 0.735 | 0.541 | 8.244 | 0.02 | Significant |
| PO | 9 | 11.164 | 0.217 | 0.838 | 0.702 | 16.529 | 0 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 161.996 | 2.219 | 0.962 | 0.926 | 88.088 | 0 | Significant |
| EN | 9 | 33.198 | 0.302 | 0.978 | 0.956 | 151.468 | 0 | Significant |
| FP | 9 | 51.729 | 1.342 | 0.963 | 0.926 | 88.162 | 0 | Significant |
| MR | 9 | 41.872 | 0.374 | 0.76 | 0.578 | 9.575 | 0.01 | Significant |
| MV | 9 | 150.949 | 0.838 | 0.645 | 0.416 | 4.982 | 0.06 | Significant |
| PO | 9 | 16.589 | 0.148 | 0.76 | 0.578 | 9.588 | 0.01 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 87.905 | 36.905 | 0.908 | 0.825 | 33.003 | 0.01 | Significant |
| EN | 9 | 25.031 | 4.87 | 0.894 | 0.798 | 27.731 | 0 | Significant |
| FP | 9 | 60939 | 22.319 | 0.908 | 0.825 | 32.986 | 0 | Significant |
| MR | 9 | 3.348 | 8.365 | 0.963 | 0.928 | 90.501 | 0 | Significant |
| MV | 9 | 44.044 | 20.421 | 0.891 | 0.795 | 27.106 | 0 | Significant |
| PO | 9 | 1.318 | 3.317 | 0.964 | 0.928 | 90.704 | 0 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 177.821 | 0.96 | 0.947 | 0.897 | 60.643 | 0 | Significant |
| EN | 9 | 34.961 | 0.132 | 0.969 | 0.94 | 109.495 | 0 | Significant |
| FP | 9 | 61.301 | 0.581 | 0.947 | 0.897 | 60.678 | 0 | Significant |
| MR | 9 | 48.115 | 0.152 | 0.704 | 0.495 | 6.865 | 0.03 | Significant |
| MV | 9 | 167.744 | 0.334 | 0.584 | 0.341 | 3.619 | 0.09 | Significant |
| PO | 9 | 19.061 | 0.06 | 0.704 | 0.496 | 6.877 | 0.03 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 111.354 | 13.867 | 0.948 | 0.898 | 61.748 | 0 | Significant |
| EN | 9 | 27.343 | 1.855 | 0.945 | 0.894 | 58.818 | 0 | Significant |
| FP | 9 | 21.11 | 8.387 | 0.948 | 0.898 | 61.739 | 0 | Significant |
| MR | 9 | 18.858 | 2.811 | 0.899 | 0.808 | 29.522 | 0 | Significant |
| MV | 9 | 87.819 | 6.67 | 0.809 | 0.654 | 13.222 | 0 | Significant |
| PO | 9 | 7.465 | 1.115 | 0.899 | 0.809 | 29.572 | 0 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 123.738 | 12.44 | 0.957 | 0.915 | 75.323 | 0 | Significant |
| EN | 9 | 28.782 | 1.671 | 0.958 | 0.918 | 77.887 | 0 | Significant |
| FP | 9 | 28.597 | 7.524 | 0.957 | 0.915 | 75.331 | 0 | Significant |
| MR | 9 | 24.545 | 2.426 | 0.873 | 0.762 | 22.437 | 0 | Significant |
| MV | 9 | 103.449 | 5.693 | 0.776 | 0.603 | 10.626 | 0.01 | Significant |
| PO | 9 | 9.72 | 0.962 | 0.873 | 0.762 | 22.47 | 0 | Significant |
DownLoad:
CSV
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 223.151 | 0.352 | 0.934 | 0.873 | 47.933 | 0 | Significant |
| EN | 9 | 40.942 | 0.049 | 0.962 | 0.925 | 86.586 | 0 | Significant |
| FP | 9 | 88.713 | 0.213 | 0.934 | 0.873 | 47.97 | 0 | Significant |
| MR | 9 | 59.754 | 0.051 | 0.632 | 0.4 | 4.665 | 0.05 | Significant |
| MV | 9 | 196.894 | 0.107 | 0.505 | 0.255 | 2.4 | 0.05 | Significant |
| PO | 9 | 23.678 | 0.02 | 0.633 | 0.4 | 4.671 | 0.05 | Significant |
DownLoad:
CSV
| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 41.3818 | 5.0133 | 25.0251 | 17.3965 | 56.9863 | 6.8934 |
| M2(G) | 40.4727 | 402053 | 24.4681 | 20.7148 | 64.279 | 8.21 |
| H(G) | 62.3993 | 8.9827 | 32.985 | 8.5408 | 38.0989 | 3.3828 |
| F(G) | 47.9859 | 4.9047 | 29.014 | 22.6488 | 68.2796 | 8.9761 |
| SS(G) | 47.5989 | 6.6252 | 28.7891 | 13.955 | 49.478 | 5.5292 |
| ReZG2(G) | 43.4975 | 5.7457 | 26.3056 | 15.5439 | 52.9971 | 6.1595 |
| ReZG3(G) | 53.2487 | 5.4722 | 32.1935 | 24.6926 | 72.5723 | 9.7875 |
DownLoad:
CSV
| Drug | Boiling point ℃ (at 760 mmHg) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 460.6 ± 40.0 | 434.88 | 428.27 | 468.02 | 429.341 | 443.60 | 437.64 | 428.71 |
| Amodiaquine | 478.0 ± 45.0 | 499.39 | 492.62 | 520.92 | 488.861 | 507.48 | 503.57 | 484.33 |
| Mefloquine | 415.7 ± 40.0 | 458.34 | 465.99 | 441.66 | 471.581 | 452.24 | 455.02 | 479.40 |
| Piperoquine | 721.1 ± 60.0 | 716.36 | 694.55 | 744.81 | 667.421 | 737.87 | 732.05 | 645.55 |
| Primaquine | 451.1 ± 45.0 | 393.84 | 394.99 | 420.05 | 394.781 | 401.21 | 398.45 | 403.37 |
| Lumefrantrine | 642.5 ± 55.0 | 657.72 | 634.64 | 691.91 | 625.181 | 671.41 | 664.04 | 606.83 |
| Atovaquine | 535.0 ± 50.0 | 587.35 | 592.48 | 553.08 | 605.981 | 574.67 | 578.56 | 597.67 |
| Pyrimethamine | 368.4 ± 52.0 | 376.24 | 383.89 | 374.53 | 390.941 | 374.10 | 375.23 | 401.26 |
| Doxycycline | 762.6 ± 60.0 | 710.49 | 747.81 | 619.94 | 761.501 | 672.30 | 690.38 | 784.23 |
DownLoad:
CSV
| Drug | Flash point (℃) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 232.23 ± 27.3 | 216.75 | 212.76 | 236.82 | 213.52 | 222.06 | 218.45 | 213.10 |
| Amodiaquine | 242.9 ± 28.7 | 255.75 | 251.68 | 268.81 | 249.54 | 260.69 | 258.32 | 246.75 |
| Mefloquine | 205.2 ± 27.3 | 230.93 | 235.58 | 220.88 | 239.08 | 227.28 | 228.96 | 243.77 |
| Piperoquine | 389.9 ± 32.9 | 386.95 | 373.80 | 404.21 | 357.61 | 400.03 | 396.52 | 344.31 |
| Primaquine | 226.6 ± 28.7 | 191.92 | 192.63 | 207.81 | 192.60 | 196.42 | 194.75 | 197.76 |
| Lumefrantrine | 342.3 ± 31.5 | 351.49 | 337.57 | 372.22 | 332.04 | 359.84 | 355.38 | 320.88 |
| Atovaquine | 277.3 ± 30.1 | 308.94 | 312.07 | 288.26 | 320.42 | 301.33 | 303.68 | 315.34 |
| Pyrimethamine | 176.6 ± 30.7 | 181.29 | 185.92 | 180.28 | 190.28 | 180.02 | 180.70 | 196.49 |
| Doxycycline | 415.0 ± 32.9 | 383.41 | 406.01 | 328.69 | 414.54 | 360.38 | 371.31 | 428.23 |
DownLoad:
CSV
| Drug | Enthalpy (kj/mol) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 72.1 ± 3.0 | 73.46 | 69.438 | 75.19 | 69.545 | 71.78 | 70.94 | 69.55 |
| Amodiaquine | 77.0 ± 3.0 | 78.476 | 78.19 | 82.17 | 77.72 | 80.33 | 79.80 | 77.3 |
| Mefloquine | 70.5 ± 3.0 | 75.28 | 74.57 | 71.71 | 75.35 | 72.94 | 73.28 | 76.61 |
| Piperoquine | 105.3 ± 3.0 | 95.34 | 105.67 | 111.71 | 102.28 | 111.15 | 110.49 | 99.74 |
| Primaquine | 71.0 ± 3.0 | 70.268 | 64.90 | 68.86 | 64.79 | 66.11 | 65.68 | 66.03 |
| Lumefrantrine | 99.6 ± 3.0 | 90.788 | 97.52 | 104.73 | 96.47 | 102.26 | 101.35 | 94.35 |
| Atovaquine | 85.4 ± 3.0 | 85.316 | 91.78 | 86.41 | 93.83 | 89.31 | 89.87 | 93.07 |
| Pyrimethamine | 61.5 ± 3.0 | 68.9 | 63.39 | 62.85 | 64.26 | 62.48 | 62.56 | 65.73 |
| Doxycycline | 116.5 ± 3.0 | 94.892 | 112.92 | 95.23 | 115.21 | 102.37 | 104.89 | 119.04 |
DownLoad:
CSV
| Drug | Molar refraction (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 97.4 ± 0.3 | 86.07 | 86.75 | 89.50 | 87.93 | 86.20 | 85.76 | 89.53 |
| Amodiaquine | 105.5 ± 0.3 | 98.08 | 97.59 | 101.49 | 97.36 | 99.15 | 98.61 | 97.59 |
| Mefloquine | 83.0 ± 0.3 | 90.43 | 93.11 | 83.53 | 94.62 | 87.96 | 89.15 | 96.88 |
| Piperoquine | 153.7 ± 0.3 | 138.48 | 131.63 | 152.24 | 125.63 | 145.86 | 143.17 | 120.95 |
| Primaquine | 80.5 ± 0.3 | 78.42 | 81.14 | 78.63 | 82.46 | 77.61 | 78.11 | 85.86 |
| Lumefrantrine | 151.0 ± 0.3 | 127.56 | 121.53 | 140.25 | 118.94 | 132.38 | 129.91 | 115.34 |
| Atovaquine | 99.5 ± 0.3 | 114.46 | 114.42 | 108.78 | 115.90 | 112.77 | 113.24 | 114.01 |
| Pyrimethamine | 67.1 ± 0.5 | 75.15 | 79.27 | 68.31 | 81.85 | 72.12 | 73.59 | 85.56 |
| Doxycycline | 109 ± 0.4 | 137.39 | 140.60 | 123.94 | 140.53 | 132.56 | 135.04 | 141.04 |
DownLoad:
CSV
| Drug | Molar volume (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZzG3(G) |
| Chloroquine | 287.9 ± 3.0 | 248.47 | 251.509 | 254.38 | 255.25 | 247.63 | 247.10 | 259.38 |
| Amodiaquine | 282.8 ± 3.0 | 276.30 | 275.811 | 283.64 | 275.96 | 278.35 | 277.27 | 276.28 |
| Mefloquine | 273.4 ± 3.0 | 258.59 | 265.755 | 239.79 | 269.94 | 251.78 | 255.05 | 274.79 |
| Piperoquine | 414.2 ± 3.0 | 369.91 | 352.069 | 407.53 | 338.08 | 389.17 | 381.83 | 325.29 |
| Primaquine | 230.3 ± 3.0 | 230.76 | 238.939 | 227.83 | 243.22 | 227.24 | 229.16 | 251.67 |
| Lumefrantrine | 422.3 ± 3.0 | 344.61 | 329.443 | 378.26 | 323.38 | 357.20 | 350.71 | 313.52 |
| Atovaquine | 271.8 ± 3.0 | 314.25 | 313.521 | 301.44 | 316.70 | 310.67 | 311.59 | 310.74 |
| Pyrimethamine | 180.2 ± 7.0 | 223.17 | 234.749 | 202.64 | 241.89 | 214.20 | 218.54 | 251.03 |
| Doxycycline | 271.1 ± 5.0 | 367.38 | 372.181 | 338.44 | 370.81 | 357.63 | 362.76 | 367.45 |
DownLoad:
CSV
| Drug | Polarizability (10-24 cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 38.6 ± 0.5 | 34.16 | 34.34 | 35.48 | 34.78 | 34.18 | 33.99 | 35.35 |
| Amodiaquine | 41.8 ± 0.5 | 38.94 | 38.64 | 40.23 | 38.50 | 39.31 | 39.09 | 38.51 |
| Mefloquine | 32.9 ± 0.5 | 35.90 | 36.86 | 33.11 | 37.42 | 34.87 | 35.33 | 38.23 |
| Piperoquine | 60.9 ± 0.5 | 54.99 | 52.10 | 60.36 | 49.66 | 57.84 | 56.76 | 47.67 |
| Primaquine | 31.9 ± 0.5 | 31.12 | 32.12 | 31.17 | 32.62 | 30.77 | 30.96 | 33.91 |
| Lumefrantrine | 59.9 ± 0.5 | 50.65 | 48.11 | 55.60 | 47.02 | 52.49 | 51.50 | 45.47 |
| Atovaquine | 39.5 ± 0.5 | 45.45 | 45.30 | 43.12 | 45.82 | 44.71 | 44.89 | 44.95 |
| Pyrimethamine | 26.6 ± 0.5 | 29.82 | 31.38 | 27.08 | 32.38 | 28.59 | 29.16 | 33.79 |
| Doxycycline | 43.2 ± 0.5 | 54.56 | 55.66 | 49.13 | 55.54 | 52.56 | 53.53 | 55.55 |
DownLoad:
CSV
| Drugs | BP (℃ at 760 mmHg) | Enthalpy (kj/mol) | Flash point (℃) | Molar refraction | Molar volume (cm3) | Polarizability |
| Chloroquine | 460.6 | 72.1 | 232.23 | 97.4 | 287.9 | 38.6 |
| Amodiaquine | 478 | 77 | 242.9 | 105.5 | 282.8 | 41.8 |
| Mefloquine | 415.7 | 70.5 | 205.2 | 83 | 273.4 | 32.9 |
| Piperoquine | 721.1 | 105.3 | 389.9 | 153.7 | 414.2 | 60.9 |
| Primaquine | 451.1 | 71 | 226.6 | 80.5 | 230.3 | 31.9 |
| Lumefrantrine | 642.5 | 99.6 | 342.3 | 151 | 422.3 | 59.9 |
| Atovaquine | 535 | 85.4 | 277.3 | 99.5 | 271.8 | 39.5 |
| Pyrimethamine | 368.4 | 61.5 | 176.6 | 67.1 | 180.2 | 26.6 |
| Doxycycline | 762.6 | 116.5 | 415 | 109 | 271.1 | 43.2 |
| Drugs | M1(G) | M2(G) | H(G) | F(G) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 106 | 120 | 10.3 | 262 | 23.9599 | 25.2333 | 584 |
| Amodiaquine | 128 | 149 | 11.7333 | 324 | 28.5664 | 30.5333 | 742 |
| Mefloquine | 114 | 137 | 9.5857 | 306 | 24.5829 | 26.631 | 728 |
| Piperoquine | 202 | 240 | 17.8 | 510 | 45.1804 | 48.9 | 1200 |
| Primaquine | 92 | 105 | 9 | 226 | 20.903 | 22.0833 | 512 |
| Lumefrantrine | 182 | 213 | 16.3666 | 466 | 40.3883 | 43.4333 | 1090 |
| Atovaquine | 158 | 194 | 12.6047 | 446 | 33.4116 | 36.5619 | 1064 |
| Pyrimethamine | 86 | 100 | 7.7667 | 222 | 18.9478 | 20.2167 | 506 |
| Doxycycline | 200 | 264 | 14.4164 | 608 | 40.4521 | 45.55 | 1594 |
| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 0.961 | 0.968 | 0.961 | 0.838 | 0.735 | 0.838 |
| M2(G) | 0.962 | 0.978 | 0.963 | 0.76 | 0.645 | 0.76 |
| H(G) | 0.908 | 0.894 | 0.908 | 0.963 | 0.891 | 0.964 |
| F(G) | 0.947 | 0.969 | 0.947 | 0.704 | 0.584 | 0.704 |
| SS(G) | 0.948 | 0.945 | 0.948 | 0.899 | 0.809 | 0.899 |
| ReZG2(G) | 0.957 | 0.958 | 0.957 | 0.873 | 0.776 | 0.873 |
| ReZG3(G) | 0.934 | 0.962 | 0.934 | 0.632 | 0.505 | 0.633 |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 124.096 | 2.932 | 0.961 | 0.923 | 83.956 | 0 | Significant |
| EN | 9 | 28.483 | 0.396 | 0.968 | 0.937 | 104.502 | 0 | Significant |
| FP | 9 | 29.813 | 1.773 | 0.961 | 0.923 | 83.973 | 0 | Significant |
| MR | 9 | 28.194 | 0.546 | 0.838 | 0.702 | 16.501 | 0.05 | Significant |
| MV | 9 | 114.389 | 1.265 | 0.735 | 0.541 | 8.244 | 0.02 | Significant |
| PO | 9 | 11.164 | 0.217 | 0.838 | 0.702 | 16.529 | 0 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 161.996 | 2.219 | 0.962 | 0.926 | 88.088 | 0 | Significant |
| EN | 9 | 33.198 | 0.302 | 0.978 | 0.956 | 151.468 | 0 | Significant |
| FP | 9 | 51.729 | 1.342 | 0.963 | 0.926 | 88.162 | 0 | Significant |
| MR | 9 | 41.872 | 0.374 | 0.76 | 0.578 | 9.575 | 0.01 | Significant |
| MV | 9 | 150.949 | 0.838 | 0.645 | 0.416 | 4.982 | 0.06 | Significant |
| PO | 9 | 16.589 | 0.148 | 0.76 | 0.578 | 9.588 | 0.01 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 87.905 | 36.905 | 0.908 | 0.825 | 33.003 | 0.01 | Significant |
| EN | 9 | 25.031 | 4.87 | 0.894 | 0.798 | 27.731 | 0 | Significant |
| FP | 9 | 60939 | 22.319 | 0.908 | 0.825 | 32.986 | 0 | Significant |
| MR | 9 | 3.348 | 8.365 | 0.963 | 0.928 | 90.501 | 0 | Significant |
| MV | 9 | 44.044 | 20.421 | 0.891 | 0.795 | 27.106 | 0 | Significant |
| PO | 9 | 1.318 | 3.317 | 0.964 | 0.928 | 90.704 | 0 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 177.821 | 0.96 | 0.947 | 0.897 | 60.643 | 0 | Significant |
| EN | 9 | 34.961 | 0.132 | 0.969 | 0.94 | 109.495 | 0 | Significant |
| FP | 9 | 61.301 | 0.581 | 0.947 | 0.897 | 60.678 | 0 | Significant |
| MR | 9 | 48.115 | 0.152 | 0.704 | 0.495 | 6.865 | 0.03 | Significant |
| MV | 9 | 167.744 | 0.334 | 0.584 | 0.341 | 3.619 | 0.09 | Significant |
| PO | 9 | 19.061 | 0.06 | 0.704 | 0.496 | 6.877 | 0.03 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 111.354 | 13.867 | 0.948 | 0.898 | 61.748 | 0 | Significant |
| EN | 9 | 27.343 | 1.855 | 0.945 | 0.894 | 58.818 | 0 | Significant |
| FP | 9 | 21.11 | 8.387 | 0.948 | 0.898 | 61.739 | 0 | Significant |
| MR | 9 | 18.858 | 2.811 | 0.899 | 0.808 | 29.522 | 0 | Significant |
| MV | 9 | 87.819 | 6.67 | 0.809 | 0.654 | 13.222 | 0 | Significant |
| PO | 9 | 7.465 | 1.115 | 0.899 | 0.809 | 29.572 | 0 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 123.738 | 12.44 | 0.957 | 0.915 | 75.323 | 0 | Significant |
| EN | 9 | 28.782 | 1.671 | 0.958 | 0.918 | 77.887 | 0 | Significant |
| FP | 9 | 28.597 | 7.524 | 0.957 | 0.915 | 75.331 | 0 | Significant |
| MR | 9 | 24.545 | 2.426 | 0.873 | 0.762 | 22.437 | 0 | Significant |
| MV | 9 | 103.449 | 5.693 | 0.776 | 0.603 | 10.626 | 0.01 | Significant |
| PO | 9 | 9.72 | 0.962 | 0.873 | 0.762 | 22.47 | 0 | Significant |
| Phy. Pro, | N | A | b | r | r2 | F | p | Indicator |
| BP | 9 | 223.151 | 0.352 | 0.934 | 0.873 | 47.933 | 0 | Significant |
| EN | 9 | 40.942 | 0.049 | 0.962 | 0.925 | 86.586 | 0 | Significant |
| FP | 9 | 88.713 | 0.213 | 0.934 | 0.873 | 47.97 | 0 | Significant |
| MR | 9 | 59.754 | 0.051 | 0.632 | 0.4 | 4.665 | 0.05 | Significant |
| MV | 9 | 196.894 | 0.107 | 0.505 | 0.255 | 2.4 | 0.05 | Significant |
| PO | 9 | 23.678 | 0.02 | 0.633 | 0.4 | 4.671 | 0.05 | Significant |
| Drugs | BP | Enthalpy | Flash point | Molar refraction | Molar volume | Polarizability |
| M1(G) | 41.3818 | 5.0133 | 25.0251 | 17.3965 | 56.9863 | 6.8934 |
| M2(G) | 40.4727 | 402053 | 24.4681 | 20.7148 | 64.279 | 8.21 |
| H(G) | 62.3993 | 8.9827 | 32.985 | 8.5408 | 38.0989 | 3.3828 |
| F(G) | 47.9859 | 4.9047 | 29.014 | 22.6488 | 68.2796 | 8.9761 |
| SS(G) | 47.5989 | 6.6252 | 28.7891 | 13.955 | 49.478 | 5.5292 |
| ReZG2(G) | 43.4975 | 5.7457 | 26.3056 | 15.5439 | 52.9971 | 6.1595 |
| ReZG3(G) | 53.2487 | 5.4722 | 32.1935 | 24.6926 | 72.5723 | 9.7875 |
| Drug | Boiling point ℃ (at 760 mmHg) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 460.6 ± 40.0 | 434.88 | 428.27 | 468.02 | 429.341 | 443.60 | 437.64 | 428.71 |
| Amodiaquine | 478.0 ± 45.0 | 499.39 | 492.62 | 520.92 | 488.861 | 507.48 | 503.57 | 484.33 |
| Mefloquine | 415.7 ± 40.0 | 458.34 | 465.99 | 441.66 | 471.581 | 452.24 | 455.02 | 479.40 |
| Piperoquine | 721.1 ± 60.0 | 716.36 | 694.55 | 744.81 | 667.421 | 737.87 | 732.05 | 645.55 |
| Primaquine | 451.1 ± 45.0 | 393.84 | 394.99 | 420.05 | 394.781 | 401.21 | 398.45 | 403.37 |
| Lumefrantrine | 642.5 ± 55.0 | 657.72 | 634.64 | 691.91 | 625.181 | 671.41 | 664.04 | 606.83 |
| Atovaquine | 535.0 ± 50.0 | 587.35 | 592.48 | 553.08 | 605.981 | 574.67 | 578.56 | 597.67 |
| Pyrimethamine | 368.4 ± 52.0 | 376.24 | 383.89 | 374.53 | 390.941 | 374.10 | 375.23 | 401.26 |
| Doxycycline | 762.6 ± 60.0 | 710.49 | 747.81 | 619.94 | 761.501 | 672.30 | 690.38 | 784.23 |
| Drug | Flash point (℃) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 232.23 ± 27.3 | 216.75 | 212.76 | 236.82 | 213.52 | 222.06 | 218.45 | 213.10 |
| Amodiaquine | 242.9 ± 28.7 | 255.75 | 251.68 | 268.81 | 249.54 | 260.69 | 258.32 | 246.75 |
| Mefloquine | 205.2 ± 27.3 | 230.93 | 235.58 | 220.88 | 239.08 | 227.28 | 228.96 | 243.77 |
| Piperoquine | 389.9 ± 32.9 | 386.95 | 373.80 | 404.21 | 357.61 | 400.03 | 396.52 | 344.31 |
| Primaquine | 226.6 ± 28.7 | 191.92 | 192.63 | 207.81 | 192.60 | 196.42 | 194.75 | 197.76 |
| Lumefrantrine | 342.3 ± 31.5 | 351.49 | 337.57 | 372.22 | 332.04 | 359.84 | 355.38 | 320.88 |
| Atovaquine | 277.3 ± 30.1 | 308.94 | 312.07 | 288.26 | 320.42 | 301.33 | 303.68 | 315.34 |
| Pyrimethamine | 176.6 ± 30.7 | 181.29 | 185.92 | 180.28 | 190.28 | 180.02 | 180.70 | 196.49 |
| Doxycycline | 415.0 ± 32.9 | 383.41 | 406.01 | 328.69 | 414.54 | 360.38 | 371.31 | 428.23 |
| Drug | Enthalpy (kj/mol) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 72.1 ± 3.0 | 73.46 | 69.438 | 75.19 | 69.545 | 71.78 | 70.94 | 69.55 |
| Amodiaquine | 77.0 ± 3.0 | 78.476 | 78.19 | 82.17 | 77.72 | 80.33 | 79.80 | 77.3 |
| Mefloquine | 70.5 ± 3.0 | 75.28 | 74.57 | 71.71 | 75.35 | 72.94 | 73.28 | 76.61 |
| Piperoquine | 105.3 ± 3.0 | 95.34 | 105.67 | 111.71 | 102.28 | 111.15 | 110.49 | 99.74 |
| Primaquine | 71.0 ± 3.0 | 70.268 | 64.90 | 68.86 | 64.79 | 66.11 | 65.68 | 66.03 |
| Lumefrantrine | 99.6 ± 3.0 | 90.788 | 97.52 | 104.73 | 96.47 | 102.26 | 101.35 | 94.35 |
| Atovaquine | 85.4 ± 3.0 | 85.316 | 91.78 | 86.41 | 93.83 | 89.31 | 89.87 | 93.07 |
| Pyrimethamine | 61.5 ± 3.0 | 68.9 | 63.39 | 62.85 | 64.26 | 62.48 | 62.56 | 65.73 |
| Doxycycline | 116.5 ± 3.0 | 94.892 | 112.92 | 95.23 | 115.21 | 102.37 | 104.89 | 119.04 |
| Drug | Molar refraction (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 97.4 ± 0.3 | 86.07 | 86.75 | 89.50 | 87.93 | 86.20 | 85.76 | 89.53 |
| Amodiaquine | 105.5 ± 0.3 | 98.08 | 97.59 | 101.49 | 97.36 | 99.15 | 98.61 | 97.59 |
| Mefloquine | 83.0 ± 0.3 | 90.43 | 93.11 | 83.53 | 94.62 | 87.96 | 89.15 | 96.88 |
| Piperoquine | 153.7 ± 0.3 | 138.48 | 131.63 | 152.24 | 125.63 | 145.86 | 143.17 | 120.95 |
| Primaquine | 80.5 ± 0.3 | 78.42 | 81.14 | 78.63 | 82.46 | 77.61 | 78.11 | 85.86 |
| Lumefrantrine | 151.0 ± 0.3 | 127.56 | 121.53 | 140.25 | 118.94 | 132.38 | 129.91 | 115.34 |
| Atovaquine | 99.5 ± 0.3 | 114.46 | 114.42 | 108.78 | 115.90 | 112.77 | 113.24 | 114.01 |
| Pyrimethamine | 67.1 ± 0.5 | 75.15 | 79.27 | 68.31 | 81.85 | 72.12 | 73.59 | 85.56 |
| Doxycycline | 109 ± 0.4 | 137.39 | 140.60 | 123.94 | 140.53 | 132.56 | 135.04 | 141.04 |
| Drug | Molar volume (cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZzG3(G) |
| Chloroquine | 287.9 ± 3.0 | 248.47 | 251.509 | 254.38 | 255.25 | 247.63 | 247.10 | 259.38 |
| Amodiaquine | 282.8 ± 3.0 | 276.30 | 275.811 | 283.64 | 275.96 | 278.35 | 277.27 | 276.28 |
| Mefloquine | 273.4 ± 3.0 | 258.59 | 265.755 | 239.79 | 269.94 | 251.78 | 255.05 | 274.79 |
| Piperoquine | 414.2 ± 3.0 | 369.91 | 352.069 | 407.53 | 338.08 | 389.17 | 381.83 | 325.29 |
| Primaquine | 230.3 ± 3.0 | 230.76 | 238.939 | 227.83 | 243.22 | 227.24 | 229.16 | 251.67 |
| Lumefrantrine | 422.3 ± 3.0 | 344.61 | 329.443 | 378.26 | 323.38 | 357.20 | 350.71 | 313.52 |
| Atovaquine | 271.8 ± 3.0 | 314.25 | 313.521 | 301.44 | 316.70 | 310.67 | 311.59 | 310.74 |
| Pyrimethamine | 180.2 ± 7.0 | 223.17 | 234.749 | 202.64 | 241.89 | 214.20 | 218.54 | 251.03 |
| Doxycycline | 271.1 ± 5.0 | 367.38 | 372.181 | 338.44 | 370.81 | 357.63 | 362.76 | 367.45 |
| Drug | Polarizability (10-24 cm3) | M1(G) | M2(G) | H(G) | F(G)) | SS(G) | ReZG2(G) | ReZG3(G) |
| Chloroquine | 38.6 ± 0.5 | 34.16 | 34.34 | 35.48 | 34.78 | 34.18 | 33.99 | 35.35 |
| Amodiaquine | 41.8 ± 0.5 | 38.94 | 38.64 | 40.23 | 38.50 | 39.31 | 39.09 | 38.51 |
| Mefloquine | 32.9 ± 0.5 | 35.90 | 36.86 | 33.11 | 37.42 | 34.87 | 35.33 | 38.23 |
| Piperoquine | 60.9 ± 0.5 | 54.99 | 52.10 | 60.36 | 49.66 | 57.84 | 56.76 | 47.67 |
| Primaquine | 31.9 ± 0.5 | 31.12 | 32.12 | 31.17 | 32.62 | 30.77 | 30.96 | 33.91 |
| Lumefrantrine | 59.9 ± 0.5 | 50.65 | 48.11 | 55.60 | 47.02 | 52.49 | 51.50 | 45.47 |
| Atovaquine | 39.5 ± 0.5 | 45.45 | 45.30 | 43.12 | 45.82 | 44.71 | 44.89 | 44.95 |
| Pyrimethamine | 26.6 ± 0.5 | 29.82 | 31.38 | 27.08 | 32.38 | 28.59 | 29.16 | 33.79 |
| Doxycycline | 43.2 ± 0.5 | 54.56 | 55.66 | 49.13 | 55.54 | 52.56 | 53.53 | 55.55 |
