IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence Information.

Retroviruses such as HIV cause significant diseases in humans and other organisms, making the discovery of antiretroviral (ARV) drugs a critical priority. While databases like ChEMBL contain valuable information, their complexity poses challenges. The data set includes approximately >140,000 assays across eight viruses, encompassing >350 biological activity parameters, >50 target proteins, >80 cell lines, >60 assay organisms, and >770 viral strains. Artificial Intelligence/Machine Learning (AI/ML) models offer a promising approach to accelerate ARV discovery. Recently, we developed AI/ML models for ChEMBL ARV data using the Information Fusion Perturbation Theory and Machine Learning (IFPTML) strategy. However, neither existing AI/ML models nor our prior IFPTML implementation simultaneously incorporates viral protein sequences, strains, cell lines, assay organisms, or virus/human mutations. This limitation renders them ineffective for predicting activity against amino acid sequence variations (e.g., mutations, variants, or emerging strains)─a critical shortcoming given the well-documented prevalence of drug-resistance mutations in marketed ARVs. In this work, we present an enhanced IFPTML model integrating protein sequence descriptors. We computed and incorporated sequence descriptors for all drug target proteins in ChEMBL, derived from proteomes of retroviruses (HIV, FeLV, MMV, SIV, etc.). The model demonstrated robust performance, with sensitivity (Sn), specificity (Sp), and accuracy (Ac) values ranging between 72.0 and 88.0% in both training and validation phases. We analyze its predictions for protein mutations documented in ChEMBL and other literature sources. To our knowledge, this represents the first unified multicondition, multioutput model for ARV discovery that systematically accounts for protein sequence information.