通过结合物理能量函数和图形神经网络来准确预测蛋白质-连接体相互作用
Yiyu Hong1, Junsu Ha1, Jaemin Sim2
1Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea.
Journal of cheminformatics
|November 4, 2024
概括
这项研究提出了一种新的计算模型,通过结合图形神经网络和基于物理的评分来预测蛋白质-连接体相互作用. 这种先进的模型显著改善了药物发现中的成功识别,在基准测试中表现优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 机器学习在药物发现中的作用
背景情况:
- 现有的基于结构的机器学习模型用于蛋白质 - 配体结合的预测,由于复杂的结合姿势,分子多样性和数据稀缺性,在虚拟选中面临局限性.
- 当前的模型通常在识别潜在药物候选人的实际应用中扎.
研究的目的:
- 开发一种先进的模型来预测蛋白质-连接体相互作用,克服现有的机器学习方法的局限性.
- 提高药物发现虚拟查的准确性和效率.
主要方法:
- 一种新的方法融合了三个独立的神经网络模型:一个用于二进制姿势预测,两个用于绑定亲和度和根-平均-平方偏差预测.
- 神经网络输出与基于物理的评分函数的集成.
- 训练解释了实验和预测的结合亲和度偏差,并造成了预测不确定性.
主要成果:
- 与现有模型相比,综合模型在命中识别方面表现显著改善.
- 在基准测试中达到32.7 (CASF2016) 和23.1 (DUD-E) 的1%的丰富系数.
- 在实验查中成功从63个候选物中确定了23种活性自毒素抑制剂,验证了其实际适用性.
结论:
- 拟议的模型整合了多个神经网络和基于物理的评分,在蛋白质-联体结合预测和击中发现方面提供了卓越的性能.
- 新的培训策略和基准结果强调了该模型的效率和通用性,以加速药物发现管道.
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