Wang Debby D, Wu Wenhui, Wang Ran
School of Science and Technology, Hong Kong Metropolitan University, 81 Chung Hau Sreet, Ho Man Tin, Hong Kong, China.
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China.
J Cheminform. 2024 Jan 3;16(1):2. doi: 10.1186/s13321-023-00795-9.
The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.
AlphaFold系列的推出将深度学习技术引入了分子结构科学领域。作为另一个关键问题，基于结构的蛋白质-配体结合亲和力预测迫切需要先进的计算技术。深度学习是否已准备好解决这个问题？在这里，我们回顾了针对此问题的主流基于结构的深度学习方法，重点关注分子表示、学习架构和模型可解释性。生成了一个模型分类法。为了弥补这些模型之间缺乏有效比较的问题，我们在统一的基础上实现并评估了代表性模型，并讨论了其优缺点。本综述可能会对基于结构的药物发现及相关领域有所助益。
