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Open Access Highly Accessed Research article

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

Hongjian Li1*, Kwong-Sak Leung1, Man-Hon Wong1 and Pedro J Ballester23

Author Affiliations

1 Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China

2 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

3 Cancer Research Center of Marseille (Inserm U1068, UM105, IPC), 27 Boulevard Lei Roure, 13009 Marseille, France

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BMC Bioinformatics 2014, 15:291  doi:10.1186/1471-2105-15-291

Published: 27 August 2014

Abstract

Background

State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients.

Results

In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study.

Conclusions

Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

Keywords:
Molecular docking; Binding affinity; Drug discovery; Machine learning