从头药物设计中分子生成模型的系统评估：从基准测试到实践见解
A Systematic Evaluation of Molecule Generation Models for De Novo Drug Design: From Benchmarks to Practical Insights
浏览论文内容
中文总结 AI 辅助
本文系统评估了82种分子生成模型，涵盖五种深度生成框架，综合比较基准性能并总结实验验证案例，提出未来研究方向以提升药物设计可靠性。
中文摘要 AI 辅助
分子生成已成为从头药物设计的一种强大计算工具，使得探索超越传统虚拟筛选局限的化学空间成为可能。该领域在分子表示、生成架构和靶点感知建模策略的推动下迅速发展。然而，现有综述通常孤立地讨论特定模型家族或应用场景，而非提供关于这些组件如何共同构成连贯生成流程的整体视角。在本综述中，我们对用于从头药物设计的分子生成模型进行了全面评估，涵盖了五种深度生成框架下的82种方法，包括基于循环神经网络（RNN）和Transformer的模型、变分自编码器（VAEs）、生成对抗网络（GANs）、基于流的模型以及扩散模型。我们首先总结了广泛使用的基准测试和分子表示，然后考察了通用生成和口袋条件生成所依据的方法论原理。本工作的一项核心贡献是对常用基准测试和评估指标上报告的性能进行系统综合和比较分析。我们还总结了具有代表性的经实验验证的案例研究。展望未来，我们讨论了标准化3D数据、相互作用感知生成、受体灵活性和多目标分子设计等未来方向，旨在提高分子生成的可靠性和实验相关性。所有收集的基准资源、评估指标和模型参考文献均可在公开访问的存储库中获取，网址为https://github.com/AITRADER/MoleculeGenerationReview。
英文摘要
Molecule generation has emerged as a powerful computational tool for de novo drug design, enabling the exploration of chemical space beyond the limits of conventional virtual screening. The field has progressed rapidly, driven by advances in molecular representations, generative architectures, and target-aware modeling strategies. However, existing reviews typically address specific model families or application scenarios in isolation, rather than offering an integrated perspective on how these components collectively form a coherent generation workflow. In this review, we present a comprehensive evaluation of molecule generation models for de novo drug design, covering 82 methods across five deep generative frameworks, including recurrent neural network (RNN)- and Transformer-based models, variational autoencoders (VAEs), generative adversarial networks (GANs), flow-based models, and diffusion models. We first summarize widely used benchmarks and molecular representations, and then examine the methodological principles underlying both general and pocket-conditioned generation. A central contribution of this work is a systematic synthesis and comparative analysis of reported performance across commonly used benchmarks and evaluation metrics. We also summarize representative experimentally validated case studies. Looking ahead, we discuss future directions in standardized 3D data, interaction-aware generation, receptor flexibility, and multi-objective molecular design, with the aim of improving the reliability and experimental relevance of molecule generation. All collected benchmark resources, evaluation metrics, and model references are provided in a publicly accessible repository at https://github.com/JacklinGroup/molecule-generation-review.
发表机构
- Xiangtan University（湘潭大学）
- Hunan University（湖南大学）
- Hong Kong Baptist University（香港浸会大学）
机构由 AI 辅助整理，请以论文原文为准。
