Performance of machine-learning scoring functions in structure-based virtual screening

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55. 6% hit rate, whereas that of Vina only 16. 2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0. 1% achieves 88. 6% hit rate for 27. 5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0. 56 and -0. 18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http: //github. com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http: //github. com/oddt/rfscorevsbinary).