SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.

ABSTRACT:

Introduction

The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machine-learning scoring functions re-evaluate the output of molecular docking to achieve more accurate predictions. However, previous scoring functions were trained on crystalised protein-ligand complexes and datasets of decoys. The limited availability of crystal structures and biases in the decoy datasets can lower the performance of scoring functions.

Objectives

To address key limitations of previous scoring functions and thus improve the predictive performance of structure-based virtual screening.

Methods

A novel machine-lea

SUBMITTER: McGibbon M 

PROVIDER: S-EPMC10105235 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature