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.

多任务学习（MTL）是一种机器学习范式，旨在通过利用跨多个任务的共享信息来增强预测模型的泛化能力。深度神经网络模型最近在各个领域取得的突破引发了化学科学领域类似进展的希望。在这篇文章中，我们深入探讨了应用于计算机辅助药物设计的神经 MTL 模型的现状和未来潜力。在药物发现的背景下，MTL 的一个突出应用是蛋白质-配体结合亲和力预测，其中单个蛋白质被视为任务。在这里，我们介绍了 MTL 的基本原理，并提出了一个根据 MTL 模型架构对 MTL 模型进行分类的框架。该框架使我们能够对已成功用于药物设计的 MTL 模型进行全面概述和比较。随后，我们深入研究了当前与 MTL 应用相关的挑战。关键挑战之一在于定义所研究的分子实体和各自的机器学习任务的合适表示。