一个可概括的深度学习框架,用于基于结构的蛋白质-连接体亲和度排名
1Department of Pharmacology, Center for AI in Protein Dynamics, Vanderbilt University, Nashville, TN 37232.
概括
一个新的深度学习框架,CORDIAL (Covolutional Representation of Distance-dependent Interactions with Attention Learning),提高了预测蛋白质-连接体结合亲缘关系的概括性. 这种方法侧重于交互签名,在新型蛋白质家族上表现优于其他机器学习模型.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 估计蛋白质 - 配体结合亲缘关系对于药物发现至关重要.
- 目前的方法在准确性和速度之间面临着一个权衡.
- 机器学习 (ML) 模型在概括性方面扎,在新型蛋白质或化学结构上失败.
研究的目的:
- 开发一种可通用的机器学习模型,用于预测蛋白质-连接体结合亲缘关系.
- 为了克服当前ML模型的局限性,这些模型在未见的数据上失败.
- 调查假设虚假的相关性阻碍了ML模型的概括性.
主要方法:
- 引入了CORDIAL (与注意力学习的距离依赖交互的共进化表示),这是一个深度学习框架.
- 设计的CORDIAL具有诱导偏差,用于学习取决于距离的物理化学相互作用特征.
- 明确避免了蛋白质和配体化学结构的直接参数化,专注于相互作用.
主要成果:
- 通过离开超级家庭的验证,Cordial 证明了持续的预测性能和校准.
- 这种方法有效地模拟了对新型蛋白质家族的预测.
- 当代ML模型在类似的验证条件下显示性能下降.
结论:
- 将特定任务的物理化学原理编码到ML架构中可以提高概括性.
- CORDIAL提供了一种经过验证的策略,用于开发基于结构的药物发现的强大,可通用的模型.
- 在CORDIAL中,只有交互的方法是有效的,用于预测跨不同蛋白质家族的结合亲和关系.
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