Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening.

ABSTRACT: 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.

SUBMITTER: Spiegel J 

PROVIDER: S-EPMC9786651 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature