Skip to main content

Diverse models for anti-HIV activity of purine nucleoside analogs



Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases. In the present study, classification models have been developed for the prediction of the anti-HIV activity of purine nucleoside analogs.


The topochemical version of superaugmented pendentic index-4 has been proposed and successfully utilized for the development of models. A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA). The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3). An in-house computer program was also employed to calculate additional MDs which were not included in the Dragon software. DT, RF, and SVM correctly classified the analogs into actives and inactives with an accuracy of 89 %, 83 %, and 78 %, respectively. MAA-based models predicted the anti-HIV activity of purine nucleoside analogs with a non-error rate up to 98 %. Therapeutic active spans of the suggested MAA-based models not only showed more potency but also exhibited enhanced safety as revealed by comparatively high values of selectivity index (SI). The statistical importance of the developed models was appraised via intercorrelation analysis, specificity, sensitivity, non-error rate, and Matthews correlation coefficient.


High predictability of the proposed models clearly indicates an immense potential for developing lead molecules for potent but safe anti-HIV purine nucleoside analogs.


The drug design and development process involves the use of a variety of computational techniques, such as (quantitative) structure-activity relationships [(Q)SAR], molecular mechanics, quantum mechanics, molecular dynamics, and drug-receptor docking [1, 2]. (Q)SAR studies are based on the premise that biological response is a function of the chemical structure [3, 4]. (Q)SAR models reveal a relationship between the structural characteristics of the compounds and their biological activity or environmental behavior [5, 6]. (Q)SAR models predict chemical behavior and simulate adverse effects in laboratory animals, tissues, and cells directly from the chemical structure. This will naturally minimize the need to conduct animal tests so as to comply with the regulatory requirements for human health and eco-toxicology endpoints [7, 8]. The main hypothesis in (Q)SAR is that similar chemicals have similar properties, and even a minor structural change(s) will result in a change in property value(s) [9]. SAR represents classification models that are used when an empirical property is characterized in a (+1/−1) manner, such as soluble/insoluble, active/inactive, toxic/non-toxic, permeable/impermeable, inhibitor/non-inhibitor, ligand/non-ligand, substrate/non-substrate, mutagen/non-mutagen, polar/non-polar, or carcinogen/non-carcinogen [1015]. In silico screening constitutes a vital cost-effective high-throughput process for providing a rapid indication of potential hazards for use in lead prioritization [16].

Machine learning (ML) constitutes a vital area of artificial intelligence (AI) in which models are simply generated by extracting rules and functions from relatively large datasets. ML comprises diverse methods and algorithms such as decision trees, general CHAID models, k-nearest neighbors, random forests, Bayesian methods, Gaussian processes, artificial neural networks (ANN), artificial immune systems, kernel algorithms, and support vector machines (SVMs). ML algorithms extract relevant information from empirical dataset through computational/statistical techniques and generate a set of rules, functions, or procedures that allow them to predict the properties of novel objects which have not been included in the learning set. (Q)SAR models derived through ML algorithms are subsequently applied during the drug development process so as to optimize the therapeutic activity, target selectivity, and related physico-chemical and biological properties of the selected molecules [10, 17, 18]. The advantage of AI approaches is that they can be easily applied to learn from examples and to evolve suitable prophesy models in spite of the limited understanding of the underlying molecular processes. The AI approach is also beneficial whenever computational simulations based on fundamental physical models are too expensive to perform [19, 20].

AIDS is one of the most urgent global health problems and is the leading cause of death in Africa and the fourth leading cause of death across the world. Highly active antiretroviral therapy (HAART) has gained considerable success in Western countries. The anti-HIV drug evolution process resembles a crystal ball and involves a plenty of astonishment, expectations, and disappointments. Unfortunately, we continue to be dependent on the predictions of the crystal ball. All of the currently available anti-HIV drugs are far from ideal, and we still face problems of acute and chronic side effects, patient compliance issues, drug resistance, cost, and potency. Hopes of long-term management and eradication depend on increasing available therapeutic options [21, 22].

Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases [23]. 9-[4-α-(Hydroxymethyl)cyclopent-2-ene-1-α-yl]guanine (CBV), (−)-β-D-(2R,4R)-1,3-dioxolane-guanosine (DXZ), 3′-azido-3′deoxy-guanosine (AZG), and 2′-C-methylguanosine are all known for their reverse transcriptase inhibiting activity [24]. 3,9-Dihydro-9-dioxo-5H-imidazo(1,2-A) purine nucleosides synthesized from these nucleosides have shown improved anti-HIV activity [25].

In the present study, models of diverse nature have been developed through decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA) using molecular descriptors (MDs) as independent variables for the prediction of the anti-HIV activity of purine nucleoside analogs in human peripheral blood mononuclear (PBM) cells.



A dataset comprising 36 purine nucleoside analogs was selected for the present investigation (Fig. 1 and Table 1). The anti-HIV activity of these analogs in human PBM cells has been reported in terms of EC50 (effective concentration against 50 % of cell population) by Amblard et al. [25]. The nucleoside analog DXZ possessing an EC50 value of 0.51 μM is well known for its anti-HIV activity. DXZ was considered as a reference compound [24]. Accordingly, analogs possessing EC50 values of ≤0.51 μM were considered to be active and analogs possessing EC50 values of >0.51 μM were considered to be inactive for the purpose of the present study.

Fig. 1

Basic structures of purine nucleoside analogs from serial number 1 to 36 [25]

Table 1 Relationship between molecular descriptors and anti-HIV activity in human PBM cells

Molecular descriptors

The MDs used in the current study include constitutional, physico-chemical, topostructural, topochemical, and topological charge indices, walk and path counts, information-based indices, and a wide variety of 3D descriptors. The majority of 2D and 3D MDs utilized in the present study were calculated using the Dragon software (version 5.3). Most of these MDs are reviewed in the textbook by Todeschini and Consonni [26]. An in-house computer program was also employed to calculate MDs which were not included in the Dragon software. Initially, MDs with significant degenerate values were omitted from the large pool of MDs calculated through both the Dragon software and the in-house computer program. For the remaining MDs, a pairwise correlation analysis was carried out (one of any two indices with r ≥ 0.90 was excluded to minimize redundant information). The abovementioned exclusion technique was utilized to decrease the correlation and collinearity between MDs. Finally, 60 MDs, enlisted in Table 2, were short-listed for the development of models.

Table 2 List of molecular descriptors

Statistical methods

Decision tree

DT is a common method that provides both classification and predictive functions simultaneously. A single DT was grown for the prediction of anti-HIV activity and to identify the importance of various MDs used for the present study. A cutoff value dividing the compounds of the dataset into active and inactive with regard to anti-HIV activity was assigned to each MD for every compound. Then, a single MD is identified that split the entire training set into two or more homogenous subsets and shows the lowest possible false assignment before being chosen as parent node. The molecules at each parent node are classified, based on the MD value, into two child nodes, and the resulting child nodes or subsets are split into sub-subsets, generally using different MDs. The majority vote of the molecules reaching the same terminal node in the training set decides the prediction for a molecule to reach a given terminal node. In this manner, DT created an interactive branching topology in which the branch taken at each intersection is determined by a rule related to a MD of the molecule, and lastly, each terminating leaf of the tree is assigned to a particular category, i.e., A (active) or B (inactive) [2730]. In the present study, RPART library was added in R program (version 2.10.1) to grow DT.

Random forest

RF is a well-known ensemble of unpruned trees generated through the systematic use of bootstrap samples of the training data for building forests (multiple trees) and random subsets of variables to facilitate the best possible bifurcation at each node [31, 32]. In the present study, the RFs were grown with the R program (version 2.10.1) using the random forest library.

Support vector machine

SVM is a relatively new classification technique. SVM involves drawing a boundary between groups of samples that fall into different classes. The SVM methodology comprised reducing the pool of 30 descriptors to a smaller size by eliminating the related variables, followed by development of classification models [33, 34]. Statistica v. 7.0 was used for the generation of SVM models. The classification models were generated using the training set of compounds followed by the validation of the best model using the test set of compounds [35]. Every third compound of the dataset was included in the test set. SVM model validity was also checked by cross-validation, i.e., leave-one-out method. SVM models were also validated by tenfold cross-validation. The kernel type that was adopted in the present work was the polynomial function. The first task was the assignment of each molecule to one class, namely ‘actives’ or ‘inactives’ based on the cutoff value (EC50 = 0.51 μM) of the reference compound.

Moving average analysis

MAA was utilized so as to facilitate the construction of single MD-based models for predicting the anti-HIV activity of purine nucleoside analogs. For the selection and evaluation of range-specific characteristics, exclusive activity ranges were determined from the frequency distribution of therapeutic response level. This was accomplished by initially plotting the relationship between index values and anti-HIV activity and subsequently identifying the active range by scrutinizing the resultant data by maximization of moving average with regard to active purine nucleoside analogs (<35 % = inactive, 35 % to 65 % = transitional, and >65 % = active) [36]. Biological activity was assigned to each analog involved in the dataset, which was subsequently compared with the reported anti-HIV activity (Table 1). Average values of EC50 and selectivity index (SI) were calculated for each range of the proposed models.

Model validation

DT-based models were validated using the tenfold cross-validation (CV) method [37]. The performance of the proposed models was evaluated by calculating the overall accuracy of prediction, sensitivity, specificity, non-error rate (arithmetic mean of sensitivity and specificity) [38, 39], and Matthews correlation coefficient (MCC) [40]. MCC is generally regarded as being one of the best statistical techniques which account for both over- and underprediction. MCC takes both sensitivity and specificity into account, and its value ranges from −1 to +1. Higher values of MCC indicate better predictions [41, 42]. The statistical importance of MDs used in building predictive models was also appraised by intercorrelation analysis. The degree of correlation was appraised by Spearman’s rank correlation coefficient ‘r’. Pairs of MDs with r ≥ 0.97 are considered to be highly inter-correlated while those with 0.68 ≤ r ≤ 0.97 to be appreciably correlated; MDs with 0.36 ≤ r ≤ 0.67 are weakly correlated whereas the pairs of MDs with low r values (<0.35) are not inter-correlated [43, 44].

Results and discussion

AIDS is the fourth leading cause of death worldwide. Inhibition of the human immunodeficiency virus and sustained suppression of viral replication reduce morbidity and prolong life in patients with HIV infection. This virus is therefore a major target for the structure-based inhibitors design.

Finding that the structure of a molecule has an important role in its therapeutic activity coupled with the need for safer potent drugs to be developed with minimum animal sacrifice, expenditure, and time loss has led to the origin of structure-activity relationship (SAR) studies [45]. The inherent problem in the development of a suitable correlation between chemical structures and biological activity can be attributed to the non-quantitative nature of chemical structures. MDs translate chemical structures into characteristic numerals and facilitate (Q)SAR studies [46, 47].

In the present study, the relationship between anti-HIV activity and the structure of purine nucleoside analogs has been investigated and suitable models developed using diverse classification techniques, i.e., DT, RF, SVM, and MAA. DT was built from a set of 60 MDs enlisted in Table 2. The MD at the originating node is the most significant, and the significance of MD decreases with the gradual increase in the tree height [2730]. The classification of purine nucleoside analogs as inactive and active using a single tree, based on the Balaban-type index from Z-weighted distance matrix, A37, JhetZ index, and mean information content on the distance magnitude, A11, IDM index, has been depicted in Fig. 2. The DT identified the Balaban-type index from Z-weighted distance matrix, A37, JhetZ index, as the most important index.

Fig. 2

A decision tree for distinguishing active purine nucleoside analogs (A) from inactive analogs (B)

A37, i.e., the Balaban-type index from Z-weighted distance matrix, JhetZ index, is based on the Barysz matrix and was developed by Barysz et al. It may be expressed as per the following:

$$ {J}_{\mathrm{B}}=\frac{q}{\left(\mu +1\right)}{\displaystyle {\sum}_{edges}^G\left(\frac{1}{\sqrt{S_i{S}_j}}\right)} $$

where S i S j represents the product of the distance sums of the adjacent pairs of vertices i and j in a graph G. The cycloatomic number of the graph is represented by μ, and it indicates the number of independent cycles in the graph [48, 49].

The DT classified the analogs with an accuracy of >99.9 % in the training set. The sensitivity, specificity, non-error rate, overall accuracy of prediction, and MCC of the tenfold cross-validated set was of the order of 75 %, 93 %, 84 %, 89 %, and 0.68, respectively (Table 3). A high value of MCC simply indicates the robustness of the proposed DT-based model.

Table 3 Confusion matrix for anti-HIV activity of purine nucleoside analogs in human PBM cells

A11, i.e., mean information content on the distance magnitude, IDM index, is one of the information indices reported by Bonchev et al. It may be expressed as per the following:

$$ {\overline{\mathrm{I}}}_D^W=-{\displaystyle {\sum}_{n=1}^G\left({k}_n\frac{n}{W}{ \log}_2\frac{n}{W}\right)} $$

where W is the Wiener index, k n is the number of distances of equal n value in the triangular submatrix D, and G is the maximum distance value [50].

The RFs were grown utilizing 60 MDs as enlisted in Table 1. The RF classified purine nucleoside analogs with regard to anti-HIV activity with an accuracy of 83 % and the out-of-bag (OOB) estimate of error was 17 %. The sensitivity, specificity, non-error rate, accuracy of prediction, and MCC value of the RF-based model for the tenfold cross-validated set were found to be 62.5 %, 89 %, 75.7 %, 83 %, and 0.52, respectively (Table 3). A high value of MCC simply indicates the robustness of the proposed RF-based model.

SVM-based classification models were built utilizing a small pool of topological descriptors as specified in the Methods section. The dataset was divided into a training and a test set based on a random test set selection comprising 27 compounds in the training and 9 compounds in the test set, respectively. The models were built using the training set molecules and subsequently validated by test set molecules. The SVM model for the training set resulted in a specificity of 100 % and an accuracy of prediction of 93 %. The sensitivity, specificity, non-error rate, overall accuracy of prediction, and MCC of the test set was of the order of 50 %, 86 %, 68 %, 78 %, and 0.36, respectively (Table 3).

Four single index-based models were developed using MAA (Table 4). The Balaban-type index from Z-weighted distance matrix: index A37, identified as the most important index by the decision tree, was used to construct a model for the prediction of the anti-HIV activity of purine nucleoside analogs. Three more indices, i.e., spherocity index, SPH, A2; shape profile no. 20, SP20, A4; and superaugmented pendentic topochemical index-4, SA P-4c, A23, were also used to develop the models for predicting the anti-HIV activity of purine nucleoside analogs.

Table 4 Proposed MAA models for the prediction of anti-HIV activity of PNAs in human PBM cells

A2, i.e., spherocity index, SPH, is one of the geometrical descriptors given by Robinson et al. and may be expressed as:

$$ {\varOmega}_{\mathrm{SPH}}=\frac{3{\lambda}_3}{\left({\lambda}_1+{\lambda}_2+{\lambda}_3\right)}\kern2em 1\ge {\varOmega}_{\mathrm{SPH}}\kern0.5em \ge \kern0.5em 0 $$

where λ 1, λ 2, and λ 3 are the eigenvalues of the auto-covarience matrix used in the principal component analysis of the molecule. The ΩSPH value ranges from unity for totally spherical molecules to zero for totally flat molecules [51].

A4, i.e., shape profile no. 20, SP20, is one of the Randic molecular profiles described by Randic and may be expressed as:

$$ S=N+{}^1Rx+{}^2R/2!{x}^2+{\kern0.5em }^3R/3!{x}^3+\kern0.5em {}^4R/4!{x}^4 \dots \kern1em \dots \kern0.5em {}^nR/n!{x}^n $$
$$ S={}^1S,\kern0.5em {}^2S,\kern0.5em {}^3S,\kern0.5em {}^4S \dots \kern1em \dots {}^nS $$

where N is a constant indicating the size of the system. 1 R, 2 R, 3 R… are the averages of the row sums in the 1 D, 2 D, 3 D… matrix, respectively. D is the geometry distance matrix of a structure [52].

A23, i.e., superaugmented pendentic topochemical index-4, is the topochemical version of the topological descriptor (superaugmented pendentic index-4) reported by Dureja et al. [53]. Superaugmented pendentic index-4 is expressed as:

$$ {}^{SA}{\displaystyle {\int}^{P-4}}\left({G}_{k,n}\right)={\displaystyle {\sum}_{i=1}^n\frac{p_i{m}_i}{e_i^4}} $$

Superaugmented pendentic topochemical index-4 may be defined as the summation of the quotients of the product of all the non-zero row elements in the chemical pendent matrix and product of chemical adjacent vertex degrees and the fourth power of the chemical eccentricity of the concerned vertex for all vertices in a hydrogen-suppressed chemical molecular graph and may be expressed as:

$$ {}^{SA}{\displaystyle {\int}^{P-4}}{\left({G}_{k,n}\right)}^c={\displaystyle {\sum}_{i=1}^n\frac{p_{ic}{m}_{ic}}{e_{ic}^4}} $$

where p ic is the chemical pendenticity and is obtained by multiplying all the non-zero row elements in the chemical pendent matrix, ∆Pc, of a chemical graph (G k, n)c. ∆Pc is a sub-matrix of the chemical distance matrix and is obtained by retaining the columns corresponding to pendent vertices. m ic is the augmented chemical adjacency and is defined as the product of chemical degrees of all the vertices v j adjacent to vertex v i . e ic is the chemical eccentricity of vertex v i , and n is the number of vertices in graph G [5456].

The results of the intercorrelation analysis (Table 5) reveal that the pairs A2:A23 and A23:A37 were not correlated while the pairs A4:A23, A4:A37, and A2:A37 were found to be weakly correlated. The accuracy of prediction for all the four MAA-based models varies from 91.7 to 96.9 %, indicating high predictability (Table 4).

Table 5 Intercorrelation matrix

The average EC50 value of the correctly predicted analogs in the active ranges in MAA-based models varied from 0.134 to 0.182 μM. Such a low average EC50 value signifies high potency of the active ranges (Fig. 3).

Fig. 3

Average EC50 of anti-HIV activity of correctly predicted PNAs in various ranges of MAA-based models

Drug safety evaluation is the key part of drug discovery and development process to identify those that have an appropriately balanced safety-efficacy profile for a given indication [57]. The therapeutic index (TI), certain safety factor (CSF), protective index (PI), therapeutic window (TW), and selectivity index (SI) are some of such important parameters that can be used to achieve this balance. TI may be defined as the ratio of LD50/ED50, where LD50 is defined as the single dose of a therapeutic agent that can be likely to cause death in 50 % of the animal population and ED50 is defined as the single dose of a therapeutic agent that can be likely to cause a particular effect to occur in 50 % of the animal population [5860]. Similarly, SI is calculated for a drug molecule in the case of cell studies, and it may be defined as the ratio of CC50 to EC50, where CC50 and EC50 represent cytotoxic and effective concentrations, respectively. It is an indirect measure of the safety of a drug. A high value of SI simply indicates low toxicity and more safety. A high value of SI is a desirable property for any drug candidate so as to minimize toxicity. Therefore, such safety parameters should be determined in the initial stages of the drug discovery process to avoid much costlier late-stage failures [61]. Active ranges of the proposed MAA-based models exhibited high degree of selectivity towards infected human PBM cells as indicated by a greater value of SI for active ranges compared to inactive ranges (Fig. 4). As a consequence, active ranges identified by MAA models have both the desired requirements of a drug molecule, i.e., high potency and safety. Model validation by confusion matrix shows the sensitivity of the models of the order of 63 % to 100 % (Table 4). High values of MCC simply indicate the robustness of the proposed MAA-based models.

Fig. 4

Average SI against PBM cells of correctly predicted PNAs in various ranges of MAA-based models

The present modeling studies may be of great utility for providing lead molecules through exploitation of active ranges in single MD-based models. The proposed models are unique and differ widely from conventional QSAR models. Both systems of modeling have their advantages and limitations. In the instant modeling, the system adopted has a distinct advantage of identification of narrow active ranges, which may be erroneously skipped during regression analysis in conventional QSAR. Since the ultimate goal of modeling is to provide lead structures, therefore, active ranges of the proposed models can play a vital role in providing lead structures [62]. Therefore, active ranges of the proposed models can naturally play a vital role in providing lead structures.


Diverse techniques such as DT, RF, SVM, and MAA were successfully used to develop models for anti-HIV purine nucleoside analogs. Models based on DT, RF, and SVM statistical approaches show an accuracy of prediction up to the order of 89 %. The overall accuracy of prediction of MAA-based models varies from 91.7 % to 96.9 % with regard to the anti-HIV activity of purine nucleoside analogs in human PBM cells. High values of sensitivity, specificity, and MCC indicate the robustness of the proposed models. Good predictability, high potency, and safety of the active ranges in the proposed MAA-based models will naturally provide ease in furnishing lead structures for the development of potent but safe anti-HIV purine nucleoside analogs.


  1. 1.

    Jurs P. Quantitative structure property relationships. In: Gasteiger J, editor. Handbook of chemoinformatics, vol. 3. Weinheim: Wiley-VCH; 2003. p. 1314–35.

    Google Scholar 

  2. 2.

    Ivanciuc O. Drug design with artificial intelligence methods. In: Meyers RA, editor. Encyclopedia of complexity and systems science. Berlin: Springer; 2009. p. 2113–39.

    Google Scholar 

  3. 3.

    Hansch C. On the structure of medicinal chemistry. J Med Chem. 1976;19(1):1–6.

    Article  CAS  Google Scholar 

  4. 4.

    Bagchi MC, Maiti BC, Mills D, Basak SC. Usefulness of graphical invariants in quantitative structure—activity correlations of tuberculostatic drugs of the isonicotinic acid hydrazide type. J Mol Model. 2004;10:102–11.

    Article  CAS  Google Scholar 

  5. 5.

    Mon J, Flury M, Harsh JB. A quantitative structure—activity relationships (QSAR) analysis of triarylmethane dye tracers. J Hydrology. 2006;316:84–97.

    Article  CAS  Google Scholar 

  6. 6.

    Sabljic A. Quantitative modeling of soil sorption for xenobiotic chemicals. Environ Health Perspect. 1989;83:179–90.

    Article  CAS  Google Scholar 

  7. 7.

    Guidance on information requirements and chemical safety assessment Chapter R.6: QSARs and grouping of chemicals. requirements r6 en.pdf. Accessed 24 May 2015

  8. 8.

    International QSAR Foundation. = 13346. Accessed 26 Aug 2012

  9. 9.

    Hansch C. A quantitative approach to biochemical structure activity relationships. Acc Chem Res. 1969;2:232–9.

    Article  CAS  Google Scholar 

  10. 10.

    Ivanciuc O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr Top Med Chem. 2008;8:1691–709.

    Article  CAS  Google Scholar 

  11. 11.

    Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model. 2006;46:1984–95.

    Article  CAS  Google Scholar 

  12. 12.

    Plewczynski D, Von Grotthuss M, Spieser SAH, Rychlewski L, Wyrwicz LS, Ginalski K, et al. Target specific compound identification using a support vector machine. Comb Chem High Throughput Screen. 2007;10:189–96.

    Article  CAS  Google Scholar 

  13. 13.

    Klon AE, Diller DJ. Library fingerprints: a novel approach to the screening of virtual libraries. J Chem Inf Model. 2007;47:1354–65.

    Article  CAS  Google Scholar 

  14. 14.

    Vogt M, Bajorath J. Introduction of an information—theoretic method to predict recovery rates of active compounds for Bayesian in silico screening: theory and screening trials. J Chem Inf Model. 2007;47:337–41.

    Article  CAS  Google Scholar 

  15. 15.

    Schneider N, Jäckels C, Andres C, Hutter MC. Gradual in silico filtering for druglike substances. J Chem Inf Model. 2008;48:613–28.

    Article  CAS  Google Scholar 

  16. 16.

    Modi S. Positioning ADMET in silico tools in drug discovery. Drug Discov Today. 2004;9:14–5.

    Article  Google Scholar 

  17. 17.

    Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics. 2004;20:2479–81.

    Article  CAS  Google Scholar 

  18. 18.

    Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco: Morgan Kaufmann; 2005. p. 525.

    Google Scholar 

  19. 19.

    Mjolsness E, DeCoste D. Machine learning for science: state of the art and future prospects. Science. 2001;293:2051–5.

    Article  CAS  Google Scholar 

  20. 20.

    Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Cur Pharm Des. 2007;13:1–12.

    Article  Google Scholar 

  21. 21.

    Stefania F, Maria LB, Laura DL, Angela R, Anna MM, Zeger D, et al. New 4-[(1-benzyl-1H-indol-3-yl)carbonyl]-3-hydroxyfuran-2(5H)-ones, β-diketo acid analogs as HIV-1 integrase inhibitors. Arch Pharm. 2007;340(6):292–8.

    Article  Google Scholar 

  22. 22.

    Glesby MJ. Drug development: an overview. 1998. ACRIA %20 Update %20 Winter %20 1998.pdf. Accessed 15 Mar 2012

  23. 23.

    Robak T, Lech-Maranda E, Korycka A, Robak E. Purine nucleoside analogs as immunosuppressive and antineoplastic agents: mechanism of action and clinical activity. Curr Med Chem. 2006;13(26):3165–89.

    Article  CAS  Google Scholar 

  24. 24.

    Parikh UM, Koontz DL, Chu CK, Schinazi RF, Mellors JW. In vitro activity of structurally diverse nucleoside analogs against human immunodeficiency virus type 1 with the K65R mutation in reverse transcriptase. Antimicrob Agents Chemother. 2005;49(3):1139–44.

    Article  CAS  Google Scholar 

  25. 25.

    Amblard F, Fromentin E, Detorio M, Obikhod A, Rapp KL, McBrayer TR, et al. Synthesis, antiviral activity and stability of nucleoside analogs containing tricyclic bases. Eur J Med Chem. 2009;44(10):3845–51.

    Article  CAS  Google Scholar 

  26. 26.

    Todeschini R, Consonni V. Handbook of molecular descriptors. Weinheim: Wiley-VCH; 2000.

    Google Scholar 

  27. 27.

    Tong W, Hong H, Fang H, Xie Q, Perkins R. Decision forest: combining the predictions of multiple independent decision tree models. J Chem Inf Comp Sci. 2003;43:525–31.

    Article  CAS  Google Scholar 

  28. 28.

    Asikainen A, Kolehmainen M, Ruuskanen J, Tuppurainen K. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVO and κNN methods. Chemosphere. 2006;62:658–73.

    Article  CAS  Google Scholar 

  29. 29.

    Wagener M, Geerestein VJ. Potential drugs and nondrugs: prediction and identification of important structural features. J Chem Inf Comp Sci. 2000;40:280–92.

    Article  CAS  Google Scholar 

  30. 30.

    Dureja H, Gupta S, Madan AK. Topological models for prediction of pharmacokinetic parameters of cephalosporins using random forest, decision tree and moving average analysis. Sci Pharm. 2008;76:377–94.

    Article  CAS  Google Scholar 

  31. 31.

    Zhang Q-U, Aires J. Random forest prediction of mutagenicity from empirical physicochemical descriptors. J Chem Inf Mod. 2007;47:1–8.

    Article  Google Scholar 

  32. 32.

    Prasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 2006;9:181–99.

    Article  Google Scholar 

  33. 33.

    Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput. 2004;14:199–222.

    Article  Google Scholar 

  34. 34.

    Vapnik V, Lerner A. Pattern recognition using generalized portrait method. Autom Remote Control. 1963;24:774–80.

    Google Scholar 

  35. 35.

    Sanchez VD. Advanced support vector machines and kernel methods. Neurocomputing. 2003;55:5–20.

    Article  Google Scholar 

  36. 36.

    Gupta S, Singh M, Madan AK. Predicting anti-HIV activity: computational approach using novel topological indices. J Comp Aided Mol Des. 2001;15:671–8.

    Article  CAS  Google Scholar 

  37. 37.

    McLachlan GJ, Do KA, Ambroise C. Analyzing microarray gene expression data. New York: Wiley; 2004.

    Google Scholar 

  38. 38.

    Han L, Wang Y, Bryant SH. Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data. BMC Bioinformatics. 2008;9:401.

    Article  Google Scholar 

  39. 39.

    Ballabio D, Consonni V. Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods. 2013;5:3790–8.

    Article  CAS  Google Scholar 

  40. 40.

    Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta. 1975;405:442–51.

    Article  CAS  Google Scholar 

  41. 41.

    Baldi P, Bruank S, Chauvin Y, Andersen CAF, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics. 2000;16:412–24.

    Article  CAS  Google Scholar 

  42. 42.

    Carugo O. Detailed estimation of bioinformatics prediction reliability through the fragmented prediction performance plots. BMC Bioinformatics. 2007;8:380.

    Article  Google Scholar 

  43. 43.

    Mason RO, Lind DA, Marchal WG. Statistics: an introduction. New York: Harcourt Brace Jovanovich; 1983.

    Google Scholar 

  44. 44.

    Congelosi VE, Taylor PE, Rice PF. Basic statistics: a real world approach. St. Paul: West Publishing Co; 1983.

    Google Scholar 

  45. 45.

    Martin YC. Quantitative drug design. New York: Dekker Press; 1978.

    Google Scholar 

  46. 46.

    Balaban AT, Motoc J, Bonchev D, Mekennyan O. Topological indices for structure—activity correlations. Top Curr Chem. 1983;114:21–55.

    CAS  Google Scholar 

  47. 47.

    Basak SC, Bertlsen S, Grunwald GD. Application of graph theoretical parameters in quantifying molecular similarity and structure activity relationships. J Chem Inf Comp Sci. 1994;34:270–6.

    Article  CAS  Google Scholar 

  48. 48.

    Balaban AT. Topological indices based on topological distances in molecular graphs. Pure Appl Chem. 1983;55:199–206.

    Article  CAS  Google Scholar 

  49. 49.

    Barysz M, Jashari G, Lall RS, Srivastava VK, Trinajstic N. On the distance matrix of molecules containing heteroatoms. In: King RB, editor. Chemical applications of topology and graph theory. Amsterdam: Elsevier; 1983. p. 222–7.

    Google Scholar 

  50. 50.

    Bonchev D, Trinajsti N. Information theory, distance matrix and molecular branching. J Chem Phy. 1977;67(10):4517–33.

    Article  CAS  Google Scholar 

  51. 51.

    Robinson DD, Barlow TW, Richards WG. Reduced dimensional representations of molecular structure. J Chem Inf Comp Sci. 1997;37:939–42.

    Article  CAS  Google Scholar 

  52. 52.

    Randic M. Molecular shape profiles. J Chem Inf Comp Sci. 1995;35:373–82.

    Article  CAS  Google Scholar 

  53. 53.

    Dureja H, Kinkar CD, Madan AK. Superaugmented pendentic indices: novel topological descriptors for QSAR/QSPR. Sci Pharm. 2009;77:521–37.

    Article  CAS  Google Scholar 

  54. 54.

    Goel A, Madan AK. Structure-activity study on anti-inflammatory pyrazole carboxylic acid hydarzide analogs using molecular connectivity indices. J Chem Inf Comp Sci. 1995;35:510–4.

    Article  CAS  Google Scholar 

  55. 55.

    Kumar V, Sardana S, Madan AK. Predicting anti-HIV activity of 2,3-diaryl-1,3-thiazolidin-4-ones: computational approach using reformed eccentric connectivity index. J Mol Mod. 2004;10:399–407.

    Article  Google Scholar 

  56. 56.

    Bajaj S, Sambi SS, Madan AK. Prediction of carbonic anhydrase activation by tri-/tetrasubstituted-pyridinium-azole drugs: a computational approach using novel topochemical descriptor. QSAR Comb Sci. 2004;23:506–14.

    Article  CAS  Google Scholar 

  57. 57.

    Muller PY, Milton MN. The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov. 2012;11:751–61.

    Article  CAS  Google Scholar 

  58. 58.

    Blazka ME. Acute toxicity and eye irritancy. In: Hayes AW, editor. Principles and methods of toxicology. 5th ed. Boca Raton: CRC Press Taylor and Francis Group; 2001. p. 1131–41.

    Google Scholar 

  59. 59.

    Kevin CO, Trevor MP. Drug toxicity and poisoning. In: Goodman LS, Gilman A, editors. The pharmacological basis of therapeutics. 12th ed. New York: The McGraw-Hill Books; 2011. p. 73–89.

    Google Scholar 

  60. 60.

    Simmons MA. Mechanisms of drug action and pharmacodynamics. In: Simmons MA, editor. Pharmacology: an illustrated review. New York: Thieme Medical Publishers; 2012. p. 26.

    Google Scholar 

  61. 61.

    Madan AK, Bajaj S, Dureja H. Classification models for safe drug molecules. In: Reisfeld B, Mayeno AN, editors. Computational toxicology, Humana Press, vol. 2. New York: Springer Science + Business Media; 2013. p. 99–102.

    Google Scholar 

  62. 62.

    Dureja H, Madan AK. Prediction of h5-HT2A receptor antagonistic activity of arylindoles: computational approach using topochemical descriptors. J Mol Graph Mod. 2006;25:373–9.

    Article  CAS  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to A K Madan.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

AKM proposed the subject, designed the study and supervised the entire work. Major work was carried out by NK. Modeling through support vector machine was carried out by VL. NK prepared the draft of the manuscript. AKM modified the manuscript. All the authors read and approved the final manuscript.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Khatri, N., Lather, V. & Madan, A.K. Diverse models for anti-HIV activity of purine nucleoside analogs. Chemistry Central Journal 9, 29 (2015).

Download citation


  • Anti-HIV activity
  • Superaugmented pendentic topochemical index
  • Balaban-type index from Z-weighted distance matrix
  • Moving average analysis
  • Purine nucleoside analogs
  • Support vector machine