浙江大学侯廷军团队报道了利用生成式人工智能进行结构调整。相关研究成果于2024年11月5日发表在《美国化学会杂志》。

将基于深度学习的分子生成模型整合到药物开发中，因其加速开发过程的潜力而受到广泛关注。其核心是导向优化，这是一个关键阶段，现有分子被精炼成可行的候选药物。随着各种深度导向优化方法的不断出现，对这些方法进行更清晰的分类至关重要。

研究人员将潜在优化方法分为两种主要类型：目标导向和结构导向。该文研究重点是结构导向优化，虽然它与实际应用高度相关，但与目标导向方法相比，它的探索较少。通过对传统计算方法的系统回顾，研究人员确定了四项特定于结构导向优化的任务：片段替换、连接体设计、支架跳跃和侧链装饰。

研究人员讨论了每项任务的动机、培训数据构建和当前发展。此外，研究人员使用经典的优化分类法对目标导向和结构导向的方法进行分类，突出了它们的挑战和未来的发展前景。最后，研究人员提出了一个参考协议，供实验化学家在结构修改任务中有效利用基于生成人工智能（GenAI）的工具，弥合方法进步和实际应用之间的差距。

附：英文原文

Title: Deep Lead Optimization: Leveraging Generative AI for Structural Modification

Author: Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Chang-Yu Hsieh, Peichen Pan, Tingjun Hou

Issue&Volume: November 5, 2024

Abstract: The integration of deep learning-based molecular generation models into drug discovery has garnered significant attention for its potential to expedite the development process. Central to this is lead optimization, a critical phase where existing molecules are refined into viable drug candidates. As various methods for deep lead optimization continue to emerge, it is essential to classify these approaches more clearly. We categorize lead optimization methods into two main types: goal-directed and structure-directed. Our focus is on structure-directed optimization, which, while highly relevant to practical applications, is less explored compared to goal-directed methods. Through a systematic review of conventional computational approaches, we identify four tasks specific to structure-directed optimization: fragment replacement, linker design, scaffold hopping, and side-chain decoration. We discuss the motivations, training data construction, and current developments for each of these tasks. Additionally, we use classical optimization taxonomy to classify both goal-directed and structure-directed methods, highlighting their challenges and future development prospects. Finally, we propose a reference protocol for experimental chemists to effectively utilize Generative AI (GenAI)-based tools in structural modification tasks, bridging the gap between methodological advancements and practical applications.

DOI: 10.1021/jacs.4c11686

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.4c11686