Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

ABSTRACT

HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.

KEYWORDS:

• Multiclass models
• HIV-integrase
• machine learning
• applicability domain
• integrase inhibitors

Availability of data and materials

All the data used to support the findings of this study are included in the article, and supplementary information is provided in the supplementary section. In addition, the data used to support the findings of this study are available from the corresponding author on request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2057588.

Additional information

Funding

This work was funded by the Researchers Supporting Project number (RSP-2021/379), King Saud University, Riyadh, Saudi Arabia.