用机器智能产生生物活性天然产品启发的分子
Petra Schneider1, Karl-Heinz Altmann2, Gisbert Schneider3
1ETH Zurich, Zurich, Switzerland. petra.schneider@sl.ethz.ch.
Chimia
|December 9, 2023
概括
机器学习通过产生灵感来自自然产品的新分子来加速药物发现. 这种"虚拟化学家"的方法有助于药物化学家创造具有所需特性的新生物活性化合物.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 计算机辅助药物设计 (CADD) 正在推进机器学习 (ML) 进行自动化分子生成.
- 机器学习的整合旨在通过人工智能增强药物化学家的创造力.
- 自然产品是新药候选人的丰富灵感来源.
研究的目的:
- 探索新药设计和目标预测的潜在应用.
- 以计算方式生成自然产品启发的生物活性化合物.
- 预测自然产品启发的药物发现的未来.
主要方法:
- 利用机器学习模型进行自动化分子生成.
- 使用"虚拟化学家"概念将天然产品转化为合成分子.
- 应用计算活动预测和自动化化合物生成.
主要成果:
- 展示ML如何从天然产品模板中创建新型,易于合成的小分子.
- 系统地将知识从天然产品转移到合成药物发现的潜力.
- 介绍了以天然产品为灵感的药物设计的前景示例.
结论:
- 机器学习显著推进了新药设计和目标预测.
- 自动生成以天然产品为灵感的化合物为药物发现提供了一个强大的新途径.
- 自然产品和计算方法之间的协同作用有望为创新的治疗方法提供未来.
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