University of Science and Technology of China, Hefei, China.
Microsoft Research AI for Science, Beijing, China.
Nat Commun. 2024 Oct 29;15(1):9360. doi: 10.1038/s41467-024-53632-4.
Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.
生成式药物设计有助于创建针对致病靶蛋白有效的化合物。这为在广阔的化学空间中发现新的化合物提供了可能性，并促进了创新治疗策略的发展。然而，生成分子的实用性通常受到限制，因为许多设计都集中在药物相关性质的狭窄范围内，未能提高后续药物发现过程的成功率。为了克服这些挑战，我们开发了 TamGen，这是一种使用 GPT 类似的化学语言模型的方法，能够实现目标感知的分子生成和化合物优化。我们证明了 TamGen 生成的化合物具有更高的分子质量和活力。此外，我们已经将 TamGen 集成到药物发现管道中，并鉴定出 14 种对结核分枝杆菌 ClpP 蛋白酶具有强烈抑制活性的化合物，其中最有效的化合物的半最大抑制浓度（IC）为 1.9μM。我们的研究结果突出了生成式药物设计方法的实际潜力和实际应用，为该领域的未来发展铺平了道路。
