通过机器学习,将低产量从弱功率解构为直接到生物学的工作流程
William McCorkindale1, Mihajlo Filep2, Nir London2
1Cavendish Laboratory, University of Cambridge UK.
RSC medicinal chemistry
|March 22, 2024
概括
这项研究引入了一种机器学习工具,用于解决直接对生物学 (D2B) 查的低产量问题,准确识别强效药物候选者. 该方法成功发现了SARS-CoV-2蛋白酶抑制剂,提高了药物发现效率.
科学领域:
- 药用化学 医学化学
- 药物发现和开发 药物发现和开发
- 计算化学计算化学
背景情况:
- 对小分子的高通量生物评估对于高效的药物发现至关重要.
- 直接对生物学 (D2B) 选通过省略净化加速化合物评估,但需要高反应产量.
- 在D2B试验中,低产率可能导致对结果的误解,将低功率误认为是假阴性.
研究的目的:
- 开发一种机器学习模型,在D2B查中将低产量与低功率脱而出.
- 为了识别由于化合物生产不足而导致的生物测试中的假阴性.
- 为了验证机器学习方法在识别有力的SARS-CoV-2主要蛋白酶抑制剂.
主要方法:
- 实现基于机器学习的收益率测试解混器.
- 使用解混器来分析D2B查数据.
- 使用SARS-CoV-2主要蛋白酶抑制剂查和分析进行验证.
主要成果:
- 机器学习模型成功地区分了低产量和低功率,识别了真正的活性化合物.
- 确定了具有纳米分子活性的有前途的SARS-CoV-2主要蛋白酶抑制剂,可与标准D2B工作流程相比较.
- 该框架在广泛的in silico屏幕中证明了其实用性,用于发现具有D2B试验可比功率的化合物.
结论:
- 机器学习可以有效地解开D2B查中的产量和功效问题,提高准确性.
- 这种方法提高了可行的候选药物的识别,特别是在PROTAC设计中.
- 开发的框架为药物发现中高效可靠的小分子评估提供了一个强大的方法.
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