Unknown

Dataset Information

0

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


ABSTRACT: 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.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.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.

SUBMITTER: Li H 

PROVIDER: S-EPMC4153907 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Li Hongjian H   Leung Kwong-Sak KS   Wong Man-Hon MH   Ballester Pedro J PJ  

BMC bioinformatics 20140827


<h4>Background</h4>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 s  ...[more]

Similar Datasets

| S-EPMC5140681 | biostudies-literature
| S-EPMC6554257 | biostudies-literature
| S-EPMC3183266 | biostudies-literature
| S-EPMC7745532 | biostudies-literature
| S-EPMC9356072 | biostudies-literature
2012-05-09 | E-GEOD-37858 | biostudies-arrayexpress
2012-05-10 | GSE37858 | GEO
| S-EPMC5099106 | biostudies-literature
2022-05-16 | GSE189510 | GEO