LAUSR

Docking‐based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand‐based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub‐optimal. With this in mind, in this work we present a method for the development of target‐specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment‐like metric. Since EOA requires target‐specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD‐E database, and used them to re‐derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target‐specific, modified functions. We then used the original ChemPLP function in small‐scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re‐docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF1%, two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA‐based optimization for the derivation of target‐specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.