Docking-based Virtual Screening with Multi-Task Learning
This work addresses the need for more efficient virtual screening in drug discovery, though it is incremental as it applies an existing machine learning technique to a specific domain.
The paper tackled the problem of docking-based virtual screening in drug discovery by applying multi-task learning to leverage data from multiple targets, resulting in improved docking score prediction and better adaptation to new targets.
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.