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Yao Zhou¹, Zhipei Sang¹, Chao Xu¹

¹Key Laboratory of Tropical Biological Resources of Ministry of Education and Hainan Engineering Research Center for Drug Screening and Evaluation, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China.

Molecular diversity

|September 4, 2025

概括

通过使用生成性人工智能实现特定部位的分子修改,MolMod加速了药物设计. 这种平台可以有效地增强化合物, 提高类似药物的性能, 并进行高精度和实验验证.

科学领域:

医学化学
计算机化药物设计
药物发现中的人工智能

背景情况:

领先优化在药物设计中至关重要,但当前的人工智能工具往往专注于新的设计,而不是针对性的修改.
现有的人工智能方法在与经过验证的化合物工作的药物化学家的实际应用上存在局限性.

研究的目的:

介绍一个基于网站的分子修改平台MolMod.
通过生成性能增强的碎片来实现化合物的目标优化.

主要方法:

开发了MolMod,一个使用ZINC20和ChEMBL碎片训练的变压器模型的平台.
通过对分子位置的用户定义的标记实现了特定位置的修改.
使用基于片段的优化来生成改进的分子片段.

主要成果:

MolMod 显示了高支架保留率和99.99%的碎片组装成功率.
在单个属性优化中达到>93%的成功率,在多个属性限制中达到95%的准确率.
实验验证显示水溶性显著改善 (α-曼戈斯:<5μg/ ml至789μg/ ml).

结论:

通过专注于特定部位的修改,MolMod为药物化学提供了一个实用的解决方案.
该平台提供ADMET配置文件和实时可视化,帮助计算和实验科学家.
通过针对性,人工智能驱动的片段优化,MolMod有效地增强了化合物的类似药物的特性.

关键词:

ADMET 的预测深度学习基于碎片的药物设计领先优化特定地点的修改

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