蛋白质语言空间中的对比学习预测了药物和蛋白质标之间的相互作用
Rohit Singh1, Samuel Sledzieski1, Bryan Bryson2,3
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
一个新的深度学习模型ConPLex准确地预测药物向相互作用,以加速药物发现. 这种方法提供了可扩展,敏感和可解释的预测,优于现有的计算方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 预测药物向相互作用通过计算加速了药物发现,但目前的方法缺乏可扩展性和灵敏性.
- 现有的技术往往会在概括性,可扩展性或灵敏性方面损害性能.
研究的目的:
- 开发一种新的深度学习模型,ConPLex,用于准确和可扩展的药物向相互作用预测.
- 通过提高准确性,适应性和特异性来改进药物发现中最先进的计算方法.
主要方法:
- 开发了ConPLex,这是一个深度学习模型,将预训练的蛋白质语言模型 (PLex) 与蛋白质固的对比性共嵌入 (Con) 结合起来.
- 利用学习表征之间的距离进行约束性预测,从而实现基因组规模选.
- 应用可解释嵌入用于可视化和蛋白质的功能特征.
主要成果:
- 康普莱克斯 (ConPLex) 证明了高精度,适应未见的数据的能力,以及对诱化合物的特异性.
- 对19种酶与药物相互作用预测的实验验证证证了12种相互作用,其中4种具有亚纳米分子亲和力.
- 可解释的嵌入方便可视化药物标空间和功能蛋白质的表征.
结论:
- 在预测药物向相互作用方面,ConPLex显著优于现有的方法.
- 该模型的可解释性和可扩展性使得在基因组规模上进行高效的in silico药物查.
- 康普莱克斯准备通过敏感和大规模的虚拟查来加速药物发现管道.
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