查询重要:选择策略如何影响药物发现中的积极学习
Huw J Williams1, Stephen D Pickett2, Andrew Baxter2
1Department of Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G11XL, Scotland.
使用SimDMTA模拟设计-制造-测试-分析 (DMTA) 周期,可以加速临床前药物发现. 积极学习中的基于不确定性的抽样与传统方法相比,可以识别出更有效的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 机器学习在药物发现中的作用
背景情况:
- 设计-制造-测试-分析 (DMTA) 周期对于临床前药物发现至关重要,但受到时间和成本的限制.
- 模拟DMTA循环可以有效地探索影响其有效性的因素.
研究的目的:
- 介绍SimDMTA,一个用于模拟DMTA循环的in silico框架.
- 评估不同的采样策略,在DMTA循环中发现和推广模型.
主要方法:
- 开发了一个模拟DMTA循环的in silico框架 (SimDMTA).
- 利用机器学习模型预测对接分数,作为生物测试的代理.
- 采用各种查询策略,包括基于不确定性的抽样,用于复合选择和代模型再训练.
- 聚焦于从3,5-二甲基-4-二化醇支架中衍生出来的分子,准基4 (BRD4) BD1结合部位.
主要成果:
- 基于不确定性的采样在发现命中中中显著超过了贪和混合方法.
- 基于不确定性的抽样增强了预测模型的概括能力.
- 到最后一次代时,排名前50名的化合物中有37个位于评估化学空间的前1%中.
- 结合随机选择的策略改善了偏差校正,但在识别顶级分子方面效率较低.
结论:
- 将分子多样性和不确定性纳入积极学习设计策略中,可以加快模型的改进,并改善命中识别.
- 基于不确定性的采样是有效的临床前药物发现模拟的优越策略.
- SimDMTA提供了一种可行的方法来探索超越传统实验限制的DMTA循环效率.
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