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贝叶斯对多个实验忠实性的优化加速了药物分子的自动发现
Matthew A McDonald1,2, Brent A Koscher1, Richard B Canty1
1Massachusetts Institute of Technology, Department of Chemical Engineering, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
ACS central science
|March 3, 2025
概括
这项研究引入了多忠实贝叶斯优化 (MF-BO) 以通过高效利用不同成本和数据质量的实验来加速药物发现. 这种新方法成功地发现了具有改进性质的新型基因素脱乙酶抑制剂.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 传统的药物发现采用实验道,连续进行选,平衡成本和吞吐量.
- 贝叶斯优化 (BO) 提供代分子优化,但缺乏用于各种实验的成本效益分析.
- 将多忠实性实验与BO结合起来,可以提高药物发现的效率.
研究的目的:
- 开发和验证用于代药物分子搜索的多忠实贝叶斯优化 (MF-BO) 方法.
- 将MF-BO与未来药物发现的自主平台集成.
- 识别具有可取性质的新型组胺脱乙酶 (HDAC) 抑制剂.
主要方法:
- 开发了一个多忠实贝叶斯优化 (MF-BO) 框架,将实验道与贝叶斯优化结合起来.
- 对各种药物标的ChEMBL数据展示了MF-BO的实用性.
- 将MF-BO集成到一个自主平台中,使用对接,百分比抑制和IC50值作为HDAC抑制剂发现的忠实级别.
主要成果:
- 在具有特定特性的化学搜索空间中,MF-BO表现出了实质性的加速.
- 该自主平台对接了超过3,500个分子,并选了120多种新型化合物.
- 确定了几种具有亚微分子抑制和没有有问题的酸盐部分的新型基因素脱乙酶抑制剂.
结论:
- 多忠实贝叶斯优化是有效和经济有效的药物发现的强大策略.
- 集成的自主平台成功识别了新的HDAC抑制剂.
- 这种方法可以发现具有改善治疗潜力和减少局限性的候选药物.
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