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Les mitochondries nouvelles cibles thérapeutiques potentielles dans le cancer du pancréas -

Aug 2014 BMC bioinformatics

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


Li H, Leung KS, Wong MH, Ballester PJ


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.

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