Integration of Graph Neural Networks and Cheminformatics Tools for Predicting HIV Inhibition

The search for new antiretroviral medicines to combat the human immunodeficiency virus (HIV) has sparked fresh approaches that span the fields of chemoinformatics and machine learning. This paper provides a unique framework for predicting the inhibitory potential of compounds against HIV by merging graph neural networks (GNNs) with the strong DeepChem, RDKit, and pytorch tools. This technique begins with the meticulous design of a controlled environment, enabled by Conda, to assure tool and library compatibility. The dataset, obtained through PyTorch Geometric, contains the SMILES and HIV inhibition data required for model building. The data is then preprocessed using DeepChem, which converts Simplified Molecular Input Line Entry System (SMILES) strings into structured molecular graphs. A GNN architecture built using Pytorch at the center of research can capture deep structural and relational patterns within molecular data. The GNN model achieves predictive strength through extensive training methods, as indicated by its high accuracy and ROC-AUC metrics. The real-world applicability of this methodology is exemplified through a user-friendly interface designed with Streamlit, which allows users to enter SMILES and receive instant predictions about HIV inhibition potential. Furthermore, the trained GNN model gives trustworthy information on the possible efficacy of exogenous compounds as HIV inhibitors. This research, which is notable for its reproducibility and practicality, has the potential to speed the identification of novel HIV drugs. By combining chemo informatics and deep learning, this research has made a significant contribution to the field of computational drug development, with far-reaching implications for solving global health issues like HIV/AIDS.