ArticleBMC chemistry2024
Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms.
Article in BMC chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predictive modeling for physicochemical properties of β-lactam antibiotics through eigenvalue based topological indices and non linear regression techniques.Scientific reports · 2026Article
- Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties.Scientific reports · 2026Article
- SMILES-based degree molecular descriptors and machine learning for QSPR modeling of anti-alkaptonuria drugs.Frontiers in chemistry · 2026Article
- Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks.Scientific reports · 2025Article
- Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis.Scientific reports · 2025Article
- On analysis of phthalocyanine network through statistical method.Scientific reports · 2024Article
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Authors and funding
6 authors.
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Abstract
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.
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