增强局部功能结构特征以改善药物向相互作用预测
Baoming Feng1, Haofan Du2, Henry H Y Tong1
1Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999708, China.
LoF-DTI通过准确预测药物向相互作用,专注于关键的局部分子特征,提高了药物发现. 这种深度学习框架提高了查效率,并确定了简化药物设计的关键结合点.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 人工智能在药物发现中的作用
背景情况:
- 分子模拟对于药物发现至关重要,但在计算上昂贵.
- 现有的药物向相互作用 (DTI) 深度学习模型往往忽略了关键的局部结构特征.
- 识别这些局部特征是理解分子识别和改善查的关键.
研究的目的:
- 开发一个深度学习框架,LoF-DTI,该框架明确地表示和配对本地功能结构特征,以增强DTI预测.
- 提高在药物发现中虚拟查的效率和准确性.
- 为药物标结合机制提供可解释的见解.
主要方法:
- 药物以分子图表 (来自SMILES) 和目标作为序列表示.
- 使用跳跃知识 (JK) 增强的图形同型网络 (GIN) 进行药物特征提取.
- 使用剩余CNN与N-mer统计数据来捕获目标动机.
- 集成了一个Gated Cross-Attention (GCA) 模块,用于原子与残留物相互作用的学习.
主要成果:
- 在多个DTI预测基准中,LoF-DTI取得了竞争性表现.
- 该框架显示,早期检索率的提高对于虚拟查至关重要.
- 案例研究成功地确定了已知的功能结合位点,验证了模型的可解释性.
结论:
- 在编码和交互方面,LoF-DTI有效地优先考虑本地特征,以便准确地预测DTI.
- 该模型提供了机制意识的指导,可能简化药物设计管道.
- LoF-DTI在利用深度学习来加速药物发现方面取得了重大进展.
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