Hybrid Canonical-Correlation Neural-Network Approach Applied to Nonnucleoside HIV-1 Reverse Transcriptase Inhibitors (HEPT Derivatives)

Peter   P.   Mager

Abstract

Beneficial antiviral HIV-1 chemotherapy is associated with adverse reactions. To optimize the desired actions and to lower the side effects of nonnucleoside HIV-1 reverse transcriptase (RT) inhibitors (NNRTIs), quantitative structure-activity relationships (QSARs) were studied by using a series of HEPT derivatives of NNRTIs. Hypothesis testing requires that certain assumptions are approximately satisfied in statistically based QSARs, however. A complementary approach is based on artificial neural network analysis. Model building can be made without the manifold assumptions of statistically based QSAR approaches but the problem is that the number of neural weights increase exponentially (danger of overfitting) under certain circumstances. A way to get more reliable results is to reduce the dimensionality of the two subsets (biological and chemical variables). A suitable method is the canonical correlation analysis. The two subsets of canonical variates are used as outputs (biologically derived variates) and inputs (chemically derived variates) of an optimized backpropagation neural network approach. The contribution summarizes the most recent results of this canonical-correlation backpropagation-neural network QSAR approach. It is shown that noncovalent interactions (lipophilic, steric, hydrogen-bonding, and inductive forces of the substituents) are responsible for the antiviral and cytotoxic actions. The outcome of this analysis produces an internally highly self-consistent result (model robustness). The predictive performance is tested. The butterfly-like conformation of the predicted compound is consistent with the butterfly-like model of other NNRTIs. Molecular simulation shows that the complexed drug interacts with the Tyr181 and Tyr188 residues of the RT. The uracil ring of the drug binds directly with Lys101, and the acyclic side chain (with an intact free hydroxyl function) binds with Lys103. The suggested noncovalent interaction forces are equivalent with that found by the QSAR analysis.

Keywords: neural-network approach, nonnucleoside hiv-1 reverse transcriptase inhibitors, hiv-1 chemotherapy

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