MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks
This work addresses the need for more efficient computational methods in drug discovery, though it appears incremental as it builds on existing single-task approaches.
The authors tackled the problem of predicting drug-target binding affinity by introducing a multi-task learning approach, which improved predictions by leveraging additional information from related tasks and regularization.
Assessing drug-target affinity is a critical step in the drug discovery and development process, but to obtain such data experimentally is both time consuming and expensive. For this reason, computational methods for predicting binding strength are being widely developed. However, these methods typically use a single-task approach for prediction, thus ignoring the additional information that can be extracted from the data and used to drive the learning process. Thereafter in this work, we present a multi-task approach for binding strength prediction. Our results suggest that these prediction can indeed benefit from a multi-task learning approach, by utilizing added information from related tasks and multi-task induced regularization.