ArticleJournal of computer-aided molecular design2019
Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.International journal of molecular sciences · 2024Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
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Authors and funding
3 authors.
Funding
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Abstract
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.
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Registered trials
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