人在循环中的积极学习,以实现目标导向的分子生成
Yasmine Nahal1,2, Janosch Menke3, Julien Martinelli4
1Department of Computer Science, Aalto University, 02150, Espoo, Finland. yasmine.nahal@aalto.fi.
Journal of cheminformatics
|December 9, 2024
概括
这项研究引入了使用主动学习和人类反来改进药物发现机器学习模型的自适应方法. 该方法改进了属性预测器,导致更准确的分子生成和更好的药物相似性,克服了现有数据的局限性.
科学领域:
- 计算化学和化学信息学.
- 药物发现中的人工智能.
- 机器学习用于预测建模.
背景情况:
- 机器学习 (ML) 模型,包括定量结构-属性关系 (QSPR) 和结构-活性关系 (QSAR) 预测器,对于通过预测分子性质来加速药物发现至关重要.
- 由于有限的训练数据,当前的ML预测器往往缺乏概括性,导致产生具有膨胀预测性质的分子,这些分子未能在实验验证中得到验证.
- 由这些预测因素指导的生成性AI代理可以探索化学空间,但容易受到这些限制的影响,产生性能差的分子.
研究的目的:
- 开发一个适应性框架,改进ML属性预测器,以实现更有效的目标导向分子生成.
- 通过整合主动学习 (AL) 和代人类反来提高ML引导药物发现的准确性和可靠性.
- 通过解决当前预测器的概括问题,改进具有可取性质的类似药物分子的生成.
主要方法:
- 积极学习 (AL) 与代反机制的整合,以改进ML属性预测器.
- 使用预期预测信息获取 (EPIG) 标准来选择分子以减少预测不确定性.
- 利用人类专家作为成本有效的反的预言,以增加有限的培训数据和完善预测器.
- 经验评估通过模拟和真实的人在循环实验.
主要成果:
- 拟议的方法成功地改进了财产预测指标,使它们更好地与预言评估保持一致.
- 在预测分子性质的准确性方面有明显的改进.
- 在排名最高的产生的分子中观察到增强的药物相似性和其他实用特性 (例如,合成可访问性).
- 该框架在人类反中显示出对噪声的稳定性.
结论:
- 开发的可适应框架有效地整合了主动学习和人类专业知识,以完善目标导向分子生成的属性预测器.
- 这种方法提高了生成AI在药物发现中的可靠性,通过确保生成的分子满足预测的配置文件,并在Oracle模型上得分高.
- 优先考虑实用的药物开发考虑,平衡化学空间探索与利用现有知识和相似性.
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