The recent application of 3D-QSAR and docking studies to novel HIV-protease inhibitor drug discovery

ABSTRACT

Introduction

Despite the availability of FDA approved inhibitors of HIV protease, numerous efforts are still ongoing to achieve ‘near-perfect’ drugs devoid of characteristic adverse side effects, toxicities, and mutational resistance. While experimental methods have been plagued with huge consumption of time and resources, there has been an incessant shift towards the use of computational simulations in HIV protease inhibitor drug discovery.

Areas covered

Herein, the authors review the numerous applications of 3D-QSAR modeling methods over recent years relative to the design of new HIV protease inhibitors from a series of experimentally derived compounds. Also, the augmentative contributions of molecular docking are discussed.

Expert opinion

Efforts to optimize 3D QSAR and molecular docking for HIV-1 drug discovery are ongoing, which could further incorporate inhibitor motions at the active site using molecular dynamics parameters. Also, highly predictive machine learning algorithms such as random forest, K-means, decision trees, linear regression, hierarchical clustering, and Bayesian classifiers could be employed.

KEYWORDS:

• HIV Protease
• HIV protease inhibitors
• 3D-QSAR
• CoMFA
• CoMSIA
• molecular docking

Article highlights

• Undesirable side effects, toxicities, and mutational resistance have limited the clinical use of available FDA approved protease inhibitors.

• There is a need for new drugs with improved inhibitory potency and reduced side effects.

• Computational simulations have been recently employed over experimental methods which are time- and resource-consuming.

• 3D-QSAR modeling is effective for predicting novel inhibitors from an existing scaffold and defining the influence of chemical properties on bioactivities

• Combinatorial molecular docking provides active site conformational details while the inclusion of other dynamical methods would improve predictive capability.

This box summarizes key points contained in the article.

Declaration of interest

The authors would like to thank the College of Health Sciences, University of KwaZulu-Natal, South Africa for infrastructural support. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This manuscript was not funded.