Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms - Ahmed, Wakeel; Zaman, Shahid; Asif, Eizzah; Ali, Kashif; Mahmoud, Emad E.; Asheboss, Mamo Abebe (BMC chemistry, 2024)
Keywords: 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