pBRICS:一种新的碎片化方法,用于对类似药物的小分子进行可解释性属性预测
Sarveswara Rao Vangala1, Sowmya Ramaswamy Krishnan1, Navneet Bung1
1TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India.
Journal of chemical information and modeling
|August 16, 2023
概括
我们开发了一种新的碎片化方法 (pBRICS),通过识别关键功能组来解释人工智能驱动的药物特性预测. 这种方法有助于药物化学家优化分子并了解模型行为,以便更好地设计药物.
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
- 人工智能在药物发现中的作用
背景情况:
- 生成型人工智能擅长设计新型分子,但预测它们的类似药物的特性和理解功能组贡献仍然具有挑战性.
- 现有的可解释的人工智能模型往往侧重于原子层次的重要性,限制了对功能组对分子性质影响的洞察力.
- 准确的属性预测对于减少晚期候选药物消耗和优化化合物至关重要.
研究的目的:
- 开发一个域意识的分子碎片化方法 (pBRICS) 来解释人工智能驱动的财产预测.
- 为了能够识别功能组对药物类似性质 (ADMET) 的贡献.
- 为药物化学家增强人工智能模型的解释性.
主要方法:
- 开发了一种新的BRICS (pBRICS) 后处理方法,将分子分解为功能组.
- 利用多任务模型来预测吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性.
- 使用梯度加权类激活映射 (Grad-CAM) 来解释片段的重要性.
- 使用匹配的分子对 (MMP) 数据集验证了方法.
主要成果:
- 该pBRICS方法成功地将分子分解为功能组,提供了对属性决定因素的见解.
- 在Grad-CAM分析中,发现了影响预测ADMET特性的显著碎片.
- 解释有助于理解MMP数据集中的错误正/负预测,其中一些错误预测由文献证明是合理的.
- 这种方法对药物化学家在识别和优化关键分子碎片方面具有实用性.
结论:
- 该pBRICS方法提供了一种新的,以功能群为中心的方法,用于在药物属性预测中解释AI.
- 可解释的人工智能,特别是通过功能组分析,可以指导分子优化和改善药物设计.
- 提高培训数据的数量,质量和多样性对于提高AI对新型分子的属性预测准确性至关重要.
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