ChemAP:通过利用多模式嵌入空间和知识蒸,在临床试验阶段之前预测化学结构的药物批准
Changyun Cho1,2, Sangseon Lee3,4, Dongmin Bang1,2
1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
Scientific reports
|October 3, 2024
概括
化学AP仅使用化学结构来预测药物批准,从而使早期药物发现决策成为可能. 这种计算模型通过有效地识别有前途的候选药物来增强资源配置.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 药物批准预测对于有效的药物研究和资源分配至关重要.
- 现有的模型通常依赖于临床数据,限制了它们在早期发展阶段的使用.
- 仅使用化学结构的计算模型是早期决策的必要条件.
研究的目的:
- 介绍ChemAP (基于化学结构的药物批准预测器),这是一种用于早期药物批准预测的新型深度学习模型.
- 利用知识蒸来丰富化学空间,从多模式数据 (临床试验,专利) 转化为基于单模式结构的表示.
- 为了使药物开发的早期阶段有效的决策,仅使用化学结构信息.
主要方法:
- 开发了ChemAP,这是一个利用知识蒸的深度学习方案.
- 在基准药物批准数据集上培训和评估ChemAP.
- 在外部数据集上验证了模型的概括性,包括最近获得FDA批准和临床试验失败的药物.
主要成果:
- 在基准数据集上,ChemAP取得了最先进的表现,AUROC为0.782和AUPRC为0.842.
- 该模型在外部数据集上展示了卓越的概括性,实现了AUROC的0.694和AUPRC的0.851.
- 在药物批准预测方面表现优于传统的机器学习和其他深度学习模型.
结论:
- 化学AP有效地预测药物批准,仅使用化学结构信息.
- 该模型有助于在药物开发过程中的早期决策.
- 这项工作首次证明了使用深度学习的结构信息预测药物批准的可能性.
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