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  • Perspective
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Neural multi-task learning in drug design


Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the generalization of predictive models by leveraging shared information across multiple tasks. The recent breakthroughs achieved by deep neural network models in various domains have sparked hope for similar advances in the chemical sciences. In this Perspective, we provide insights into the current state and future potential of neural MTL models applied to computer-assisted drug design. In the context of drug discovery, one prominent application of MTL is protein–ligand binding affinity prediction, in which individual proteins are considered tasks. Here we introduce the fundamental principles of MTL and propose a framework for categorizing MTL models on the basis of their architecture. This framework enables us to present a comprehensive overview and comparison of a selection of MTL models that have been successfully utilized in drug design. Subsequently, we delve into the current challenges associated with the applications of MTL. One of the key challenges lies in defining suitable representations of the molecular entities under investigation and the respective machine learning tasks.

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Fig. 1: Pipeline for pair-input MTL models.
Fig. 2: Illustrative representations of molecules and proteins.

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M. Hilleke, I. Pachón Angona, L. Cotos Muñoz, A. Sotiropoulou, J. Ledergerber, H. Wetton, M. Iff, C. Schiebroek, F. Lohmann, A. Ilnicka, P. Schneider, C. Isert and K. Atz are thanked for helpful discussions. This research was supported by the Swiss National Science Foundation (grants 205321_182176, 1-007655-000 and P500PT_214430).

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G.S. declares a potential financial conflict of interest as co-founder of, Zurich, and in his role as scientific consultant to the pharmaceutical industry. The other authors declare no competing interests.

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Allenspach, S., Hiss, J.A. & Schneider, G. Neural multi-task learning in drug design. Nat Mach Intell 6, 124–137 (2024).

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