Docking-based Virtual Screening with Multi-Task Learning

TL;DR

This paper demonstrates that multi-task learning improves docking score prediction and target adaptation in virtual screening, potentially accelerating drug discovery processes.

Contribution

It introduces multi-task learning to docking-based virtual screening, leveraging data across multiple targets to enhance prediction accuracy and adaptability.

Findings

Multi-task learning outperforms single-task models in docking score prediction.

Models trained with multi-task learning adapt better to new targets.

Multi-task learning benefits other drug discovery tasks like drug-target affinity prediction.

Abstract

Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data, in this work, we apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multiple targets, the model trained by multi-task learning shows a better ability to adapt to a new target. Additional empirical study shows that other problems in drug discovery, such as the experimental drug-target affinity prediction, may also benefit from multi-task learning. Our results demonstrate that multi-task learning is a promising machine learning approach for docking-based virtual screening and accelerating the process of drug discovery.