在分子pKi预测中使用主动学习方法
I M Kashafutdinova1, A Poyezzhayeva1, T Gimadiev1
1A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, 420008, Russia.
Molecular informatics
|August 6, 2024
概括
积极学习 (AL) 通过尽量减少试验来优化药物发现. 一个新的混合战略平衡了勘探和利用,有效地识别了具有所需特性的候选药物,并确保了分类任务中的高模型性能.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 识别有力的候选药物需要进行广泛的查.
- 测量庞大的化学图书馆是资源密集的.
- 积极学习 (AL) 提供了一种优化候选人选择和尽量减少实验试验的策略.
研究的目的:
- 为了对药物发现的各种AL策略进行基准测试.
- 确定一种最佳的AL方法,以实现高模型性能和高效的分子选择.
- 制定一个统一的AL战略,平衡勘探和开采.
主要方法:
- 模拟活跃学习 (AL) 工作流程使用虚拟实验.
- 利用已知分子生物活性值的 ChEMBL 数据集.
- 提出并评估了具有可调节参数 (n 和 c) 的混合AL选择策略.
主要成果:
- 在分类任务中,探索和混合策略 (c<1为n=1,c≤0.2为n=2) 用最小的数据构建了高性能模型.
- 对于物业选择,开发和混合策略 (c≥1对于n=1,c≥0.7对于n=2) 是有效的.
- 混合策略c=0.7有效地平衡了模型性能和物业选择.
结论:
- 积极学习显著减少了早期药物设计所需的试验数量.
- 拟议的混合AL策略提供了可适应和高效的分子选择.
- 这种方法提高了具有所需特性和预测模型准确性的候选药物的识别.
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