Getting Docking into Shape Using Negative Image-Based Rescoring
The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in the structure-based drug discovery. To remedy this problem, elaborate rescoring and post-processing schemes have been developed with a varying degree of success, specificity, and cost. The negative imagebased rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets.The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein’s ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is dir…
Improving Docking Performance Using Negative Image-Based Rescoring
Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing th…