在分子对上使用主动学习找到最强效的化合物
Zachary Fralish1, Daniel Reker1
1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Beilstein journal of organic chemistry
|September 3, 2024
概括
ActiveDelta通过配对化合物来增强分子优化的积极学习,以提高功效和支架的多样性,特别是在有限的数据的情况下. 这种适应性方法通过更快地识别更好的候选药物来加速药物发现.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 积极学习加速了分子优化,但在早期阶段与有限的数据作斗争,可能产生具有低架构多样性的类似物.
- 剥削性的积极学习可以导致模型过度适应初始数据,限制对新化学空间的探索.
研究的目的:
- 引入ActiveDelta,一种适应性主动学习方法,使用配对的分子表示来预测改进和指导数据采集.
- 评估ActiveDelta在提高已识别的分子抑制剂的功效和化学多样性的有效性.
主要方法:
- 应用了基于图形的深度 (Chemprop) 和基于树的 (XGBoost) 模型进行主动学习.
- 在99K的基准测试数据集上评估了性能,将ActiveDelta与标准的积极学习方法 (Chemprop,XGBoost,Random Forest) 进行比较.
- 化学多样性使用Murcko支架进行评估.
主要成果:
- 在识别更强大的抑制剂方面,ActiveDelta的实施显著超过了标准的积极学习.
- 该方法成功识别了基于Murcko支架的更大的化学多样性分子抑制剂.
- 用ActiveDelta选择的数据训练的深度学习模型 (Chemprop) 在模拟的时间分割测试集中显示出更好的准确性.
结论:
- ActiveDelta代表了积极学习策略的重大进步,特别是在低数据场景中.
- 像ActiveDelta这样的分子配对方法可以加速识别强效和多样化的候选药物.
- 这种方法具有很大的潜力,可以改善针对关键药物目标的击中识别.
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