Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms

• Published in: BMC chemistry Vol. 18; no. 1; pp. 167 - 22
• Main Authors: Ahmed, Wakeel, Zaman, Shahid, Asif, Eizzah, Ali, Kashif, Mahmoud, Emad E., Asheboss, Mamo Abebe
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 12.09.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Anti-HIV agents; Anti-HIV-1 drugs; Antiviral agents; Biological activity; Biological effects; Biological properties; Chemical properties; Chemistry; Chemistry and machine learning; Chemistry and Materials Science; Chemistry/Food Science; Data mining; Drug discovery; Drug therapy; Drugs; Effectiveness; Forecasts and trends; HIV; HIV (Viruses); Human immunodeficiency virus; Machine learning; Machine learning algorithm; Molecular structure; Physicochemical properties; Python; Python algorithm; QSPR analysis; Supervised learning; Topological indices; Topology; Iran; Anti-HIV-1 drugs; QSPR analysis; Python algorithm; Topological indices; Machine learning algorithm
• ISSN: 2661-801X; 2661-801X
• DOI: 10.1186/s13065-024-01266-4

In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets. In addition to improving our comprehension of the links between molecular structure and effectiveness, the collaboration between machine learning and QSPR research further highlights the potential of computational approaches in drug discovery. This work reveals the mechanisms underlying anti-HIV effectiveness, which paves the way for the development of more potent anti-HIV drugs. This work reveals the mechanisms underlying anti-HIV efficiency, which paves the way for the development of more potent anti-HIV drugs which demonstrates the invaluable advantages of machine learning in assessing drug properties by clarifying the biological processes underlying anti-HIV behavior, which paves the way for the design and development of more effective anti-HIV drugs.