MIFS:一种自适应的多路径信息融合自主监督的药物发现框架
1Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
概括
这项研究引入了一个可适应的多路径信息融合自主监督框架 (MIFS),用于人工智能驱动的药物发现. MIFS从未标记的数据中增强了分子表示,改善了预测并提供了化学洞察力.
科学领域:
- 人工智能的人工智能
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能驱动的药物发现面临着在有限的标记数据下生成有表现力的分子表示的挑战.
- 当前的方法往往忽视分子内的多样化信息传播,在预训练期间缺乏化学约束.
研究的目的:
- 开发一种可适应的多路径信息融合自主监督框架 (MIFS),以改进分子表示学习.
- 解决药物发现中的分子编码器现有预训练策略的局限性.
主要方法:
- 提出了一种可适应的多路径信息融合自主监督框架 (MIFS),使用一种新型分子图形编码器 (Mol-EN).
- 为了全面的语义理解,Mol-EN采用了三种信息传播途径 (原子对原子,键对原子,组对原子).
- 在1100万个未标记的分子上实施了适应性预训练策略,使用拓对比损失,并在微调过程中纳入了元素知识图 (ElementKG).
主要成果:
- 在14个药物发现基准数据集中,MIFS实现了竞争性表现,包括属性预测和相互作用预测任务.
- 该框架展示了为其预测提供化学上可信的解释的能力.
- 在大型未标记数据集上进行预训练,采用基于支架的策略和ElementKG集成,提高了分子表示质量.
结论:
- 拟议的MIFS框架有效地为人工智能驱动的药物发现产生表达性的分子表示.
- MIFS提供了一种有希望的方法来克服数据稀缺性,并将化学知识纳入分子建模.
- 该方法为制药研究中更可解释和更准确的AI模型提供了基础.
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