人工智能用于预测生物活动,并使用立体化学信息生成分子命中
Tiago O Pereira1, Maryam Abbasi2,3,4, Rita I Oliveira5,6
1Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal. top@dei.uc.pt.
Journal of computer-aided molecular design
|October 17, 2023
概括
这项研究引入了一种深度学习方法,用于设计向药物化合物. 它使用强化学习和注意力来预测分子结合亲和力,成功地以高精度识别了USP7的潜在抑制剂.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 人工智能在医学中的应用
背景情况:
- 预测分子结合亲和力对于药物发现至关重要.
- 整合立体化学信息可以提高预测的准确性.
- 深度学习模型为复杂的分子性质预测提供了潜力.
研究的目的:
- 开发一种深度学习方法,用于生成有针对性的打击化合物.
- 为了预测与生物标的结合亲和力,考虑立体化学.
- 为了确定乌比基特异蛋白酶7 (USP7) 的新型抑制剂.
主要方法:
- 利用深度强化学习和注意力机制.
- 开发了一个深度的"预测者"模型,将化学结构与结合亲和关系联系起来 ([公式:见文本]).
- 评估了分子描述符 (ECFP4,ECFP6,SMILES,RDK指纹) 和注意力机制.
- 采用了自适应的多目标优化策略.
- 嵌入式立体异构体用于3D结构生成和预测.
主要成果:
- 使用双向循环神经网络和注意力的SMILES描述符的预测器获得了最佳性能.
- 确定了与USP7活性部位相互作用的关键分子区域.
- 产生了具有高预测生物亲和力和最佳立体化学构造的可合成分子.
结论:
- 深度学习,特别是注意力机制和SMILES描述符,对于预测结合亲和力和生成候选药物是有效的.
- 立体化学信息至关重要,可以成功地集成到预测和生成过程中.
- 这种方法使得我们能够发现新的,高亲和度的,和立体化学定义的抑制剂,用于治疗目标,如USP7.
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