Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

Basheerulla Shaik; Tabassum Zafar; Vijay K Agrawal

doi:10.1155/2013/795621

. 2013 Dec 30;2013:795621. doi: 10.1155/2013/795621

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

Basheerulla Shaik ^1,^*, Tabassum Zafar ², Vijay K Agrawal ¹

PMCID: PMC4207402 PMID: 25379290

Abstract

The present study deals with the estimation of the anti-HIV activity (log1/C) of a large set of 107 HEPT analogues using molecular descriptors which are responsible for the anti-HIV activity. The study has been undertaken by three techniques MLR, ANN, and SVM. The MLR model fits the train set with R ²=0.856 while in ANN and SVM with higher values of R ² = 0.850, 0.874, respectively. SVM model shows improvement to estimate the anti-HIV activity of trained data, while in test set ANN have higher R ² value than those of MLR and SVM techniques. R _m ² = metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O), and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C) present set of compounds.

1. Introduction

Undoubtedly Human immunodeficiency virus (HIV) infection is considered to be a deadly disease by the international community including the World Health Organization (WHO), UNAIDS. The WHO in its reports has said that AIDS has killed more than 25 million people since 1981 which is most the destructive pandemics in the history.

It is also a well-known fact that a lentivirus (a member of the retrovirus family) causes acquired immunodeficiency syndrome (AIDS) [1, 2], damaging immune system and leading to life-threatening infections. A report published in 2007 reveals that approximately 36 million people suffered due to HIV infection. An estimated 2.1 million people were even killed that year including 330,000 children. Another study also reveals that 2.5 million people developed new infections [3–6]. Unfortunately the number of deaths is still rising due to this deadly disease.

Just to overcome the problem scientists are working day and night and a number of RT inhibitors including various nonnucleoside RT inhibitors (NNRTIs) have been discovered as new anti-HIV agents. They have better blocking potential and have been proved to be effective [7–9]. These compounds 1-[2-Hydroxyethoxy) methyl]-6-(phenylthio)-thymine (HEPT) are known for targeting enzyme allosteric site which are less toxic and found to have more stable than nucleoside RT inhibitors.

Many efforts have been made to model the anti-HIV activity of HEPT derivatives in the past using 2D, 3D, and holographic (HQSAR) methods [10–13]. Quantitative structure activity relationship studies were carried out in order to build models for the estimation of binding affinities (ΔG _b) of HEPT and nevirapine analogues with reverse transcriptase [14]. Similarly, Agrawal et al. [15, 16] have successfully reported use of physicochemical as well as topological indices for modeling anti-HIV activities of HEPT analogues.

In continuation to these studies we now report modeling of anti-HIV activity of 1-[2-Hydroxyethoxy) methyl]-6-(phenylthio)-thymine (HEPT) derivatives (Figure 1) using graph theoretical descriptors in which distances and connectivity have been considered. The general structure of HEPT compounds used in the present study is demonstrated in Figure 1. The structural details are presented in Table 1. This Table also shows the experimental anti-HIV activity of compounds.

General structure of HEPT compounds used in the present study.

Table 1.

Structural details of the compounds with their anti-HIV activity (log1/ $C$ ) values used in the present study.


Comp. No	R₁	R₂	R₃	X	Obs. log 1/ $C$	Est. log 1/ $C$ (MLR)	Residual (MLR)

1	2-Me	Me	CH₂OCH₂CH₂OH	O	4.150	4.685	−0.535
2	2-NO₂	Me	CH₂OCH₂CH₂OH	O	3.850	4.495	−0.645
3	2-OMe	Me	CH₂OCH₂CH₂OH	O	4.720	4.831	−0.111
4	3-Me	Me	CH₂OCH₂CH₂OH	O	5.590	5.520	0.070
5	3-Et	Me	CH₂OCH₂CH₂OH	O	5.570	5.612	−0.042
6	3-t-Bu	Me	CH₂OCH₂CH₂OH	O	4.920	4.891	0.029
7	3-CF₃	Me	CH₂OCH₂CH₂OH	O	4.350	5.011	−0.661
8	3-F	Me	CH₂OCH₂CH₂OH	O	5.480	5.132	0.348
9	3-Cl	Me	CH₂OCH₂CH₂OH	O	4.890	5.544	−0.654
10	3-Br	Me	CH₂OCH₂CH₂OH	O	5.240	4.821	0.419
11	3-I	Me	CH₂OCH₂CH₂OH	O	5.000	5.037	−0.037
12	3-NO₂	Me	CH₂OCH₂CH₂OH	O	4.470	3.976	0.494
13	3-OH	Me	CH₂OCH₂CH₂OH	O	4.090	4.882	−0.792
14	3-OMe	Me	CH₂OCH₂CH₂OH	O	4.660	4.507	0.153
15	3,5-Me₂	Me	CH₂OCH₂CH₂OH	O	6.590	6.792	−0.202
16	3,5-Cl₂	Me	CH₂OCH₂CH₂OH	O	5.890	5.372	0.518
18	3-COOMe	Me	CH₂OCH₂CH₂OH	O	5.100	4.033	1.067
19	3-COMe	Me	CH₂OCH₂CH₂OH	O	5.140	4.296	0.844
20	3-CN	Me	CH₂OCH₂CH₂OH	O	5.000	4.952	0.048
21	H	CH₂CH=CH₂	CH₂OCH₂CH₂OH	O	5.600	5.849	−0.249
22	H	Et	CH₂OCH₂CH₂OH	S	6.960	6.793	0.167
24	H	i-Pr	CH₂OCH₂CH₂OH	S	7.230	7.380	−0.150
25	3,5-Me₂	Et	CH₂OCH₂CH₂OH	S	8.110	7.691	0.419
27	3,5-Cl₂	Et	CH₂OCH₂CH₂OH	S	7.370	6.996	0.374
28	H	Et	CH₂OCH₂CH₂OH	O	6.920	6.595	0.325
30	H	i-Pr	CH₂OCH₂CH₂OH	O	7.200	7.982	−0.782
31	3,5-Me₂	Et	CH₂OCH₂CH₂OH	O	7.890	7.794	0.096
32	3,5-Me₂	i-Pr	CH₂OCH₂CH₂OH	O	8.570	8.727	−0.157
33	3,5-Cl₂	Et	CH₂OCH₂CH₂OH	O	7.850	6.822	1.028
35	H	Me	CH₂OCH₂CH₂OH	O	5.150	5.015	0.135
36	H	Me	CH₂OCH₂CH₂OH	S	6.010	5.218	0.792
37	H	I	CH₂OCH₂CH₂OH	O	5.440	5.443	−0.003
41	H	CH=CPh₂	CH₂OCH₂CH₂OH	O	6.070	6.118	−0.048
42	H	Me	CH₂OCH₂CH₂OMe	O	5.060	5.329	−0.269
44	H	Me	CH₂OCH₂CH₂OCOPh	O	5.120	5.058	0.062
45	H	Me	CH₂OCH₂Me	O	6.480	5.020	1.460
46	H	Me	CH₂OCH₂CH₂Cl	O	5.820	5.247	0.573
47	H	Me	CH₂OCH₂CH₂N₃	O	5.240	5.397	−0.157
48	H	Me	CH₂OCH₂CH₂F	O	5.960	4.919	1.041
49	H	Me	CH₂OCH₂CH₂Me	O	5.480	5.522	−0.042
50	H	Me	CH₂OCH₂Ph	O	7.060	6.175	0.885
52	H	Et	CH₂OCH₂Me	S	7.580	7.221	0.359
53	3,5-Me₂	Et	CH₂OCH₂Me	O	8.240	8.030	0.210
54	3,5-Me₂	Et	CH₂OCH₂Me	S	8.300	8.293	0.007
56	3,5-Me₂	Et	CH₂OCH₂Ph	O	8.550	8.856	−0.306
59	H	i-Pr	CH₂OCH₂Me	O	7.990	7.623	0.367
60	H	i-Pr	CH₂OCH₂Ph	O	8.510	8.549	−0.039
61	H	i-Pr	CH₂OCH₂Me	S	7.890	7.718	0.172
62	H	i-Pr	CH₂OCH₂Ph	S	8.140	8.479	−0.339
63	H	Me	CH₂OMe	O	5.680	5.341	0.339
64	H	Me	CH₂OBu	O	5.330	5.405	−0.075
65	H	Me	Et	O	5.660	6.443	−0.783
66	H	Me	Bu	O	5.920	6.171	−0.251
67	3,5-Cl₂	Et	CH₂OCH₂Me	S	7.890	6.913	0.977
68	H	Et	CH₂O–i-Pr	S	6.660	6.841	−0.181
69	H	Et	CH₂O–c-Hex	S	5.790	6.395	−0.605
70	H	Et	CH₂OCH₂–c-Hex	S	6.450	7.042	−0.592
71	H	Et	CH₂OCH₂C₆H₄(4-Me)	S	7.110	7.152	−0.042
72	H	Et	CH₂OCH₂C₆H₄(4-Cl)	S	7.920	7.182	0.738
73	H	Et	CH₂OCH₂CH₂Ph	S	7.040	6.522	0.518
75	H	Et	CH₂O–i-Pr	O	6.470	6.478	−0.008
76	H	Et	CH₂O–c-Hex	O	5.400	6.208	−0.808
77	H	Et	CH₂OCH₂–c-Hex	O	6.350	7.016	−0.666
78	H	Et	CH₂OCH₂CH₂Ph	O	7.020	6.354	0.666
79	H	c-Pr	CH₂OCH₂Me	S	7.020	7.115	−0.095
80	H	c-Pr	CH₂OCH₂Me	O	7.000	6.865	0.135
84	4-F	Me	CH₂OCH₂CH₂OH	O	3.600	4.192	−0.592
85	4-Cl	Me	CH₂OCH₂CH₂OH	O	3.600	4.002	−0.402
88	4-OH	Me	CH₂OCH₂CH₂OH	O	3.560	3.526	0.034
92	3-CONH₂	Me	CH₂OCH₂CH₂OH	O	3.510	4.475	−0.965
93	H	COOMe	CH₂OCH₂CH₂OH	O	5.180	4.652	0.528
94	H	CONHPh	CH₂OCH₂CH₂OH	O	4.740	5.393	−0.653
95	H	SPh	CH₂OCH₂CH₂OH	O	4.680	5.176	−0.496
96	H	CCH	CH₂OCH₂CH₂OH	O	4.740	5.408	−0.668
97	H	CCPh	CH₂OCH₂CH₂OH	O	5.470	5.015	0.455
99	H	COCHMe₂	CH₂OCH₂CH₂OH	O	4.920	5.579	−0.659
100	H	COPh	CH₂OCH₂CH₂OH	O	4.890	5.085	−0.195
101	H	CCMe	CH₂OCH₂CH₂OH	O	4.720	5.130	−0.410
102	H	F	CH₂OCH₂CH₂OH	O	4.000	3.886	0.114
103	H	Cl	CH₂OCH₂CH₂OH	O	4.520	4.214	0.306
104	H	Br	CH₂OCH₂CH₂OH	O	4.700	4.460	0.240
105	H	Me	CH₂OCH₂CH₂OCH₂Ph	O	4.700	5.157	−0.457
106	H	Me	H	O	3.600	4.575	−0.975
107	H	Me	Me	O	3.820	4.535	−0.715

Test
17	3,5-Me₂	Me	CH₂OCH₂CH₂OH	S	6.660	7.005	−0.345
23	H	Pr	CH₂OCH₂CH₂OH	S	5.000	6.644	−1.644
26	3,5-Me₂	i-Pr	CH₂OCH₂CH₂OH	S	8.300	8.499	−0.199
29	H	Pr	CH₂OCH₂CH₂OH	O	5.470	6.532	−1.062
34	4-Me	Me	CH₂OCH₂CH₂OH	O	3.660	4.382	−0.722
38	H	CH=CH₂	CH₂OCH₂CH₂OH	O	5.690	6.366	−0.676
39	H	CH=CHPh	CH₂OCH₂CH₂OH	O	5.220	5.726	−0.506
40	H	CH₂Ph	CH₂OCH₂CH₂OH	O	4.370	5.183	−0.813
43	H	Me	CH₂OCH₂CH₂OAc	O	5.170	4.385	0.785
51	H	Et	CH₂OCH₂Me	O	7.720	7.080	0.640
55	H	Et	CH₂OCH₂Ph	O	8.230	7.271	0.959
57	H	Et	CH₂OCH₂Ph	S	8.090	7.184	0.906
58	3,5-Me₂	Et	CH₂OCH₂Ph	S	8.140	8.940	−0.800
74	3,5-Cl₂	Et	CH₂OCH₂Me	O	8.130	6.881	1.249
81	H	Me	CH₂OCH₂CH₂OC₅H₁₁	O	4.460	4.538	−0.078
82	2-Cl	Me	CH₂OCH₂CH₂OH	O	3.890	4.738	−0.848
83	3-CH₂OH	Me	CH₂OCH₂CH₂OH	O	3.530	4.726	−1.196
86	4-NO₂	Me	CH₂OCH₂CH₂OH	O	3.720	3.835	−0.115
87	4-CN	Me	CH₂OCH₂CH₂OH	O	3.600	4.794	−1.194
89	4-OMe	Me	CH₂OCH₂CH₂OH	O	3.600	3.588	0.012
90	4-COMe	Me	CH₂OCH₂CH₂OH	O	3.960	4.208	−0.248
91	4-COOH	Me	CH₂OCH₂CH₂OH	O	3.450	3.765	−0.315
98	3-NH₂	Me	CH₂OCH₂CH₂OH	O	3.600	5.115	−1.515

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A close look of Figure 1 and the activity data presented in Table 1 indicates that the anti-HIV activity mainly dependent upon the type and number of substituent R ₁ in the benzene moiety.

2. Materials and Methods

2.1. Experimental Data

The structural details as well as anti-HIV activity (log1/C) of 107 HEPT analogues are reported in Table 1. The RT inhibition data in terms of log1/C have been taken from the literature [12]. All the chemical structures were drawn with the help ACD labs software which helps in the calculation of topological descriptors. These descriptors were calculated using Dragon software using mol file generated by Chem sketch software.

2.2. Selection Molecular Descriptors and Training/Test Set for External Validation

In the present study for estimating the anti-HIV activity of 107 HEPT analogues we have used a pool of descriptors classified into 20 different groups. The descriptor selection is carried out by stepwise regression analysis (forward selection method using NCSS ver. 8 [17]. These selected descriptors are recorded in Table S1 (see Supplementary Material available online at http://dx.doi.org/10.1155/2013/795621). The data set was divided into training and test sets using random sampling technique in which 80% (84 compounds) of the data is taken as training set and the remaining 20% (23 compounds) as test set for the MLR, ANN, and SVM analyses.

3. Results and Discussion

The data (Table 2) was subjected to regression analysis which subsequently gave a correlation matrix showing intercorrelation among the selected descriptors and also with the anti-HIV (log1/C) activity. The same has been presented in Table S2. The variable selection for multiple regression analysis has indicated the possibility of using only ten models for modeling the anti-HIV activity (log1/C). These models are reported in Table S3. All these models are generated as a result of successive addition of one to ten descriptors. However, correlation of number of descriptors present in the model with R ² (Figure 2) indicated that at the most we can use only four to five descriptors for obtaining statistically most significant model. The statistically significant models obtained from MLR analysis are reported below.

Table 2.

Brief description of the descriptors used in the present study.

S. number	Symbol	Descriptor type	Meaning
1	MATS5e	2D autocorrelation	Moran autocorrelation—lag 5/weighted by atomic Sanderson's electronegativities
2	RDF080u	RDF descriptors	Radial distribution function 8.0/unweighted
3	T(O⋯O)	Topological descriptors	The sum of topological distance between (O⋯O)
4	MATS5m	2D autocorrelation	Moran autocorrelation—lag 5/weighted by atomic masses

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Correlation of variable count against R ².

3.1. MLR Results

3.1.1. One-Variable Model

Successive regression analysis indicated that in one-variable model Moran autocorrelation—lag 5/weighted by atomic Sanderson electronegativites (MATS5e) as correlating descriptor is the best model for modelling the log1/C. This model is as follows:

\begin{matrix} \log \frac{1}{C} = - 10.9800 (\pm 0.9083) MATS 5 e + 4.6503 \\ N = 84, Se = 0.8423, R^{2} = 0.6406, \\ F -ratio = 146.132, Q = 0.9501 . \end{matrix}

(1)

Here and hereafter N is number of compounds, Se is standard error, R ² is squared correlation coefficient, R _A ² is adjusted R ², F-ratio is Fishers ratio, and Q is Pogliani quality factor [18–20].

The negative coefficient of MATS5e indicates that the decrease in its magnitude will enhance the activity (log1/C).

3.1.2. Two-Variable Model

When (RDF080u) the unweighted radial distribution function 8.0 is added to the above model the model shows significant improvement in all the parameters. The R ² value changes from 0.6406 to 0.7402. Similarly improvement in adjusted R ² also shows that the addition of (RDF080u) parameter is justified. The improved model is as follows:

\begin{matrix} \log \frac{1}{C} = - 9.5891 (\pm 0.8161) MATS 5 e \\ + 0.0899 (\pm 0.0161) RDF 080 u + 3.5920, \\ N = 84, Se = 0.7205, R^{2} = 0.7402, \\ R_{A}^{2} = 0.7337, F -ratio = 115.364, Q = 1.1940 . \end{matrix}

(2)

The above model indicates that decrease in MATS5e and increase in RDF080u will improve the log1/C values.

3.1.3. Three-Variable Model

When T(O ⋯ O), which is a parameter which takes care of distance between O atom, is added to the previously stated two-parametric model a three-parametric model is yielded as below. Here the change in R ² and also Q value suggests that the model is better than the earlier one:

\begin{matrix} \log \frac{1}{C} = - 7.9071 (\pm 0.7818) MATS 5 e \\ + 0.0979 (\pm 0.0141) RDF 080 u \\ - 0.0162 (\pm 0.0031) T (O \dots O) + 4.1554, \\ N = 84, Se = 0.6277, R^{2} = 0.8053, \\ R_{A}^{2} = 0.7980, F -ratio = 110.273, Q = 1.4295 . \end{matrix}

(3)

Here the negative coefficient of T(O ⋯ O) indicates that the decrease in topological distance between (O ⋯ O) will favour the exhibition of the anti-HIV activity (log1/C).

3.1.4. Four-Variable Model

Addition of Moran auto correlation—lag 5/weighted by atomic masses MATS5 m to the above three-parametric model yielded a four-parametric model. A drastic improvement in variance is observed (R ² changes from 0.8053 to 0.8566) The model is given as follows:

\begin{matrix} \log \frac{1}{C} = - 5.0974 (\pm 0.8573) MATS 5 e \\ + 0.0862 (\pm 0.0124) RDF 080 u \\ - 0.0150 (\pm 0.0027) T (O \dots O) \\ - 3.2147 (\pm 0.6045) MATS 5 m + 4.2720, \\ N = 84, Se = 0.5420, R^{2} = 0.8566, \\ R_{A}^{2} = 0.8493, F -ratio = 117.976, Q = 1.7075 . \end{matrix}

(4)

Here the coefficient of MATS5 m is negative. This indicates that lower value of MATS5 m will favour the log1/C value for the compounds used in the present study.

A close look at (4) reveals that MATS5e (Moran autocorrelation—lag 5/weighted by atomic Sanderson electronegativites) and MATS5 m (Moran auto correlation—lag 5/weighted by atomic masses) play dominant role in exhibiting the activity. They belong to 2D autocorrelation category. The brief description of the descriptors is given in Table 2.

The predicted log1/C values of training set compounds using the above four-parametric model are recorded in Table 1 and plotted against their experimental values. Such a correlation is demonstrated in Figure S1. The above reported model (4) has further been used to predict the log1/C values of remaining 23 compounds which are in test set. Such predicted values are also recorded in Table 1. The predicted R ² value for the model has been obtained by plotting a graph between observed and estimated log1/C values for the compounds and is demonstrated in Figure S1. The R _pred ². comes out to be 0.814 confirming that the proposed model is meaningful.

The above findings confirm that for the estimation of anti-HIV activity (log1/C) of present set of compounds a four-variable model containing MATS5e, RDF080u, T(O ⋯ O), and MATS5m as correlating descriptors is the most appropriate model. The Ridge analysis (Table 3) indicates that all the Ridge parameters are well within the allowed values indicating that the proposed model is most suitable and statistically significant. Ridge trace and variance inflation factor for the four variable model were recorded in Figures 3 and 4, respectively.

Table 3.

Ridge regression parameters for (4).

Model number	Parameters used	VIF	λ _i	T	K
(4)	MATS5e	2.1512	2.1261	0.4649	1.0000
	RDF080u	1.1535	0.9763	0.8670	2.1800
	T(O⋯O)	1.2186	0.6042	0.8206	3.5200
	MATS5m	2.0363	0.2932	0.4911	7.2500

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VIF: variance inflation factor; λ _i: eigenvalue; T: tolerance; K: condition number.

These four descriptors were further used in artificial neural network (ANN) and support vector machine (SVM) techniques. However the methodology, validation techniques, and model performance evaluation by these two methods is previously discussed by Agrawal et al. [21–23]. The observed and predicted values of log1/C of the training as well as the test data using the ANN and SVM techniques are reported in Table S4.

3.2. ANN and SVM Results

Artificial neural network (ANN) and support vector machine (SVM) analyses were carried out using STATISTICA Data Miner software Ver. 10 [24]. The initial architecture of the ANN selected was four neurons in the input layer and three neurons in the hidden layer and one output neuron selected by automated network search function. The input neurons correspond to four selected descriptors of the best MLR model. The optimization was done with 10-fold cross-validation. When the entire training data is trained in the network it gives R ² = 0.850, RMSE = 2.193, and MAE = 0.24. Using the trained network the test set was used for prediction and gives R ² = 0.878, RMSE = 0.823, and MAE = 0.171. A plot of the observed and predicted values of log1/C of the training as well as the test data using the ANN model is shown in Figure S2.

The SVM regression type 1 was selected for training the data to obtain capacity C and Epsilon (ε) and gamma (γ) values. In order to find the optimum values of two parameters (γ and ε), the tenfold cross-validation based on the training set was performed and values giving the lowest RMSE were selected. Using the selected parameters (γ = 0.14, ε = 0.20, and C = 110) final training run was carried out on entire training set resulting in predicted log1/C values. The statistical parameters of this model come out to be RMSE = 0.148, R ² = 0.874, and MAE = 0.01 for the training set. This SVM model is used to predict log1/C values of the test set. The predicted statistical values of SVM model are RMSE = 1.87, R ² = 0.867, and MAE = 0.393. A plot of the observed and predicted values of log1/C of the training as well as the test set using the SVM model is shown in Figure S3.

The average r _m ² and Δr _m ² were calculated for judging the quality of the proposed model using r _m ² metric method. It is well established that for an acceptable QSAR model the value of “average r _m ²” should be >0.5 and “Δr _m ²” should be <0.2 [25, 26]. In our study two different variants of this parameter, r _m ² and Δr _m ², were calculated for both the training (internal validation) and test (external validation) sets in addition to the total dataset (overall validation). The r _m ², Δr _m ² values for all the training, test, and overall data set (MLR, ANN, and SVM) are reported in Table 4.

Table 4.

Values of the r ² _m metrics judging the quality of the model.

S. number	Method	Sets	Average r ² _m	Δ r ² _m
1	MLR	Train	0.799	0.114
		Test	0.597	0.170
		Overall	0.732	0.133

2	ANN	Train	0.825	0.079
		Test	0.671	0.133
		Overall	0.799	0.045

3	SVM	Train	0.817	0.102
		Test	0.561	0.182
		Overall	0.731	0.125

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A close observation of this table clearly indicates that all the values obtained from the r _m ² metrics are in favour of the four-parametric model proposed by us.

Randomization test is performed to investigate the probability of chance correlation for the best models. Generally in randomization test the dependent variable (log1/C) is randomly shuffled and new QSAR models are investigated using the original descriptors. After performing the test, the results indicate that the coefficient of determination obtained by chance is low while the RMSE values are high. This clearly indicates that the models obtained in this study are better than those obtained by chance. The randomization test results are shown in Figure S4.

3.3. Comparison with Other QSAR Studies

Luco and coworkers [12] proposed QSAR-based multiple regression analysis and pls methods for anti-HIV activity of 107 HEPT analogues. They developed QSAR-based models on the entire dataset and found that the best model involves 11 correlating descriptors with statistical quality given by R ² = 0.9044. It is interesting to compare our results with the results of Luco and coworkers. Our model is with four correlating parameters having the R ² = 0.856 in training set and R _Pred ² = 0.814 in test case. MLR technique is better than the previously reported one by Luco et al.; in addition to that we have also applied ANN (artificial neural network) and SVM (support vector machine) techniques in which the statistical parameters are better especially with ANN method.

4. Conclusions

A comparison of results from the model performance demonstrates that the SVM model predicts the binding affinity of the compounds more accurately than ANN and MLR models for the train dataset. While for test set prediction, ANN model was better. The proposed models could identify and provide some important information which is responsible for anti-HIV activity. These models could be used for designing new HEPT derivatives.

Supplementary Material

Table S1: Calculated values of the descriptors used in the present study.

Table S2: Correlation matrix.

Table S3: Variable selection for multiple regression analysis.

Table S4: Observed and estimated log 1/C values of eq 4 using ANN and SVM techniques.

Table S5: Values of the r² _m metrics judging the quality of the model.

Figure S1: Correlation between observed and estimated values of log 1/C using eq. 4.

Figure S2: Correlation between observed and estimated values of log 1/C using eq. 4. (ANN).

Figure S3: Correlation between observed and estimated values of log 1/C using eq. 4. (SVM).

795621.f1.pdf^{(102.8KB, pdf)}

Acknowledgments

Basheerulla Shaik is thankful to NITTTR, Bhopal, for providing the Research facilities. One of the authors (Tabassum Zafar) is thankful to Department of Science & Technology (DST) for providing the INSPIRE fellowship no. IF110673.

Conflict of Interests

The authors report no conflict of interests.

Disclosure

The authors are responsible for writing of the paper.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1: Calculated values of the descriptors used in the present study.

Table S2: Correlation matrix.

Table S3: Variable selection for multiple regression analysis.

Table S4: Observed and estimated log 1/C values of eq 4 using ANN and SVM techniques.

Table S5: Values of the r² _m metrics judging the quality of the model.

Figure S1: Correlation between observed and estimated values of log 1/C using eq. 4.

Figure S2: Correlation between observed and estimated values of log 1/C using eq. 4. (ANN).

Figure S3: Correlation between observed and estimated values of log 1/C using eq. 4. (SVM).

795621.f1.pdf^{(102.8KB, pdf)}

[B1] 1.Weiss RA. How does HIV cause AIDS? Science. 1993;260(5112):1273–1279. doi: 10.1126/science.8493571. [DOI] [PubMed] [Google Scholar]

[B2] 2.Douek DC, Roederer M, Koup RA. Emerging concepts in the immunopathogenesis of AIDS. Annual Review of Medicine. 2009;60:471–484. doi: 10.1146/annurev.med.60.041807.123549. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] 3. Joint United Nations Programme on HIV/AIDS. Overview of the global AIDS epidemic (PDF). Report on the global AIDS epidemic, 2006.

[B4] 4. UNAIDS, WHO (December 2007). 2007 AIDS epidemic update.

[B5] 5.Levin A, Hayouka Z, Friedler A, Loyter A. Specific eradication of HIV-1 from infected cultured cells. AIDS Research and Therapy. 2010;7, article 31 doi: 10.1186/1742-6405-7-31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B6] 6.Heravi MJ, Parastar F. Use of Artificial neural networks in a qsar study of anti-hiv activity for a large group of HEPT derivatives. Journal of Chemical Information and Computer Sciences. 2000;40(1):147–154. doi: 10.1021/ci990314+. [DOI] [PubMed] [Google Scholar]

[B7] 7.De Clercq E. HIV-1-specific RT inhibitors: highly selective inhibitors of human immunodeficiency virus type 1 that are specifically targeted at the viral reverse transcriptase. Medicinal Research Reviews. 1993;13(3):229–258. doi: 10.1002/med.2610130303. [DOI] [PubMed] [Google Scholar]

[B8] 8.Young SD. Non-nucleoside inhibitors of HIV-1 reverse transcriptase. Perspectives in Drug Discovery and Design. 1993;1(1):181–192. [Google Scholar]

[B9] 9.Miyasaka T, Tanaka H, Baba M, et al. A novel lead for specific anti-HIV-1 agents: 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine. Journal of Medicinal Chemistry. 1989;32(12):2507–2509. doi: 10.1021/jm00132a002. [DOI] [PubMed] [Google Scholar]

[B10] 10.Hannongbua S, Nivesanond K, Lawtrakul L, Pungpo P, Wolschann P. 3D-quantitative structure-activity relationships of HEPT derivatives as HIV-1 reverse transcriptase inhibitors, based on ab initio calculations. Journal of Chemical Information and Computer Sciences. 2001;41(3):848–855. doi: 10.1021/ci0001278. [DOI] [PubMed] [Google Scholar]

[B11] 11.Polanski J, Gieleciak R, Magdziarz T, Bak A. GRID formalism for the comparative molecular surface analysis: application to the CoMFA benchmark steroids, azo dyes, and HEPT derivatives. Journal of Chemical Information and Computer Sciences. 2004;44(4):1423–1435. doi: 10.1021/ci049960l. [DOI] [PubMed] [Google Scholar]

[B12] 12.Luco JM, Ferretti FH. QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. Journal of Chemical Information and Computer Sciences. 1997;37(2):392–401. doi: 10.1021/ci960487o. [DOI] [PubMed] [Google Scholar]

[B13] 13.Pungpo P, Hannongbua S, Wolschann P. Hologram quantitative structure-activity relationships investigations of non-nucleoside reverse transcriptase inhibitors. Current Medicinal Chemistry. 2003;10(17):1661–1677. doi: 10.2174/0929867033457106. [DOI] [PubMed] [Google Scholar]

[B14] 14.Singh J, Shaik B, Mishra KC, Agrawal VK, Khadikar PV. Topological estimation of binding affinities (ΔGb) for hept and nevirapine analogues with HIV-I reverse transcriptase. A molecular connectivity approach. Oxidation Communications. 2009;32(1):13–27. [Google Scholar]

[B15] 15.Agrawal VK, Singh J, Mishra KC, Khadikar PV. QSAR study on cytotoxic activities of a series of HEPT analogues. Letters in Drug Design and Discovery. 2006;3(2):129–137. [Google Scholar]

[B16] 16.Agrawal VK, Singh J, Karmarkar S, Khadikar PV. Qsar studies on some anti HIV-1 drugs: deoxy analogues of HEPT(1) Oxidation Communications. 2006;29(4):793–802. [Google Scholar]

[B17] 17.Hintze J. NCSS 8, NCSS, LLC, Kaysville, Utah, USA, 2012 http://www.ncss.com/

[B18] 18.Pogliani L. Structure property relationships of amino acids and some dipeptides. Amino Acids. 1994;6(2):141–153. doi: 10.1007/BF00805842. [DOI] [PubMed] [Google Scholar]

[B19] 19.Pogliani L. Modeling with special descriptors derived from a medium-sized set of connectivity indices. Journal of Physical Chemistry. 1996;100(46):18065–18077. [Google Scholar]

[B20] 20.Pogliani L. From molecular connectivity indices to semiempirical connectivity terms: recent trends in graph theoretical descriptors. Chemical Reviews. 2000;100(10):3827–3858. doi: 10.1021/cr0004456. [DOI] [PubMed] [Google Scholar]

[B21] 21.Louis B, Singh J, Shaik B, Agrawal VK, Khadikar PV. QSPR and ANN studies on prediction of aqueous solubility of heterogeneous set of organic compounds. International Journal of Pure and Applied Chemistry. 2008;3(4):259–274. [Google Scholar]

[B22] 22.Louis B, Agrawal VK, Khadikar PV. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses. European Journal of Medicinal Chemistry. 2010;45(9):4018–4025. doi: 10.1016/j.ejmech.2010.05.059. [DOI] [PubMed] [Google Scholar]

[B23] 23.Louis B, Agrawal VK. Quantitative Structure-pharamacokinetic relationship (QSPkR) analysis of the volume of distribution values of anti-infective agents from J group of the ATC classification in humans. Acta Pharmaceutica Sinica B. 2012;62:305–323. doi: 10.2478/v10007-012-0024-z. [DOI] [PubMed] [Google Scholar]

[B24] 24.StatSoft. Inc. (2011). STATISTICA (data analysis software system), version 10, http://www.statsoft.com.

[B25] 25.Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN. Some case studies onapplication of “r 2 m” metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. Journal of Chemical Information and Computational Science. 2013;34:1071–1082. doi: 10.1002/jcc.23231. [DOI] [PubMed] [Google Scholar]

[B26] 26.Golbraikh A, Tropsha A. Beware of q 2! Journal of Molecular Graphics and Modelling. 2002;20(4):269–276. doi: 10.1016/s1093-3263(01)00123-1. [DOI] [PubMed] [Google Scholar]

PERMALINK

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

Basheerulla Shaik

Tabassum Zafar

Vijay K Agrawal

Abstract

1. Introduction