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Machine Learning to Predict Binding Affinity

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Docking Screens for Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2053))

Abstract

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.

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Acknowledgments

This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).

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  1. Escola de Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul—PUCRS, Porto Alegre, RS, Brazil

    Gabriela Bitencourt-Ferreira & Walter Filgueira de Azevedo Jr.

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  1. Gabriela Bitencourt-Ferreira
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  2. Walter Filgueira de Azevedo Jr.
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Correspondence to Walter Filgueira de Azevedo Jr. .

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  1. Escola de Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul—PUCRS, Porto Alegre, Rio Grande do Sul, Brazil

    Walter Filgueira de Azevedo Jr.

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Bitencourt-Ferreira, G., de Azevedo, W.F. (2019). Machine Learning to Predict Binding Affinity. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_16

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