Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: an integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulation

• Autor(a) principal: Cobre, Alexandre de Fátima
• Data de Publicação: 2024
• Outros Autores: Ara, Anderson, Alves, Alexessander Couto, Neto, Moisés Maia, Fachi, Mariana Millan, Beca, Laize Botas, Tonin, Fernanda, Pontarolo, Roberto
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.21/17630
• topic: HIV; CCR5; Drug discovery; Machine learning; Molecular docking; Molecular dynamics

Recent data from the World Health Organization reveals that in 2023, 38.8 million people were living with HIV. Within this population, there were 1.5 million new cases and 650 thousand deaths attributed to the disease. This study employs an integrated approach involving QSAR-based machine learning models, molecular docking, and molecular dynamics simulations to identify potential compounds for inhibiting the bioactivity of the CC chemokine receptor type 5 (CCR5) protein, a key entry point for HIV. Using non-redundant experimental data from the CHEMBL database, 40 different machine learning algorithms were trained and the top four models (XGBoost, Histogram gradient Boosting, Light Gradient Boosted Machine, and Extra Trees Regression) were utilized to predict anti-HIV bioactivity for 37 billion compounds in the ZINC-22 database. The screening resulted in the identification of 124 new anti-HIV drug candidates, confirmed through molecular docking and dynamics simulations. The study underscores the therapeutic potential of these compounds, paving the way for further in vitro and in vivo investigations. The convergence of machine learning and experimental findings presents a promising avenue for significant advancements in pharmaceutical research, particularly in the treatment of viral diseases such as HIV. To guarantee the reproducibility of our study, we have made the Python code (Google Collab) and the associated database available on GitHub. You can access them through the following link: GitHub Link: https://github.com/AlexandreCOBRE/code