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

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 algo...

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Bibliographic Details
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
Online Access:	Get full text
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