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Assessing drug-target affinity is a critical step in the drug discovery anddevelopment process, but to obtain such data experimentally is both timeconsuming and expensive. For this reason, computational methods for predictingbinding strength are being widely developed. However, these methods typicallyuse a single-task approach for prediction, thus ignoring the additionalinformation that can be extracted from the data and used to drive the learningprocess. Thereafter in this work, we present a multi-task approach for bindingstrength prediction. Our results suggest that these prediction can indeedbenefit from a multi-task learning approach, by utilizing added informationfrom related tasks and multi-task induced regularization.

MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks