发现了针对3CL蛋白质酶的共价化合物,其侧面相互作用突起神经网络
Zhihao Gu1,2, Yong Yan1, Hanwen Liu1
1Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
Journal of chemical information and modeling
|March 23, 2024
概括
研究人员开发了一个深度学习模型,使用横向相互作用尖端神经网络 (LISNN) 来有效选共价药物. 这种方法成功地确定了SARS-CoV-2 3CL Pro的强效抑制剂,证明了其对大规模共价化合物发现的潜力.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 相对于非共价药物,共价药物具有独特的优势.
- 联对接对于化合物选至关重要,但由于需要确定反应类型,在大规模应用中面临挑战.
研究的目的:
- 开发一种快速且可扩展的选共价化合物的方法.
- 构建一个深度学习模型来预测共价结合和抑制活性.
主要方法:
- 利用横向交互尖端神经网络 (LISNN) 进行深度学习.
- 开发了分类模型来预测对SARS-CoV-2 3CL Pro的共价结合和抑制活性.
- 在共价复合体和化合物抑制活性数据集上训练模型.
主要成果:
- 获得了对共价结合和抑制活性的高预测准确性 (ACC > 0.9).
- 选了一个化合物库,确定了32种潜在的抑制剂.
- 发现一种与369.5nM的IC50对抗SARS-CoV-2 3CL Pro的化合物,具有共价结合和标选择性的证据.
结论:
- 基于LISNN的共价化合物选模型是有效和合理的.
- 这种方法使得对共价化合物的有效大规模选成为可能.
- 已识别的化合物显示出作为SARS-CoV-2 3CL Pro. 3CL Pro. 的抑制剂的承诺.
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