Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions
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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.
KeywordsChemical descriptors Nonparametric descriptors MERA Scoring function Machine learning
This work was supported by the Government of the Russian Federation (Act 211, contract 02.A03.21.0011) and by the Ministry of Education and Science of the Russian Federation (Grants 4.8298.2017/8.9, 4.7279.2017/8.9).
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