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基于序列的药物设计作为计算药物设计中的一个概念

Lifan Chen^1,2, Zisheng Fan^1,3,4, Jie Chang^1,3

¹Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.

Nature communications

|July 14, 2023

概括

这项研究引入了一种新的序列到药物方法,用于使用蛋白质序列和可微分学习进行计算药物设计. 该方法提供了传统基于结构的药物设计 (SBDD) 的替代方案,特别是对于缺乏3D结构的蛋白质.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

传统的基于结构的药物设计 (SBDD) 是一个复杂的,多步骤的过程.
SBDD严重依赖于高质量的3D蛋白质结构的可用性.
开发用于具有挑战性的标或未知结构的蛋白质的药物仍然是一个重大障碍.

研究的目的:

提出和验证用于计算药物设计的新型序列到药物概念.
开发一种基于端到端差分学习的计算工具,用于药物发现.
证明蛋白序列信息在识别候选药物和标中的有用性.

主要方法:

开发了TransformerCPI2.0,这是一个深度学习模型,利用端到端可差分学习.
在蛋白质序列数据上训练并验证了模型,用于药物向相互作用预测.
应用该模型来发现具有挑战性的目标的新药命中,并确定了现有药物的新目标.

主要成果:

变压器CPI2.0在各种蛋白质和化合物中表现出强大的泛化能力.
该模型成功地确定了难以向的蛋白质的新药候选者.
该概念的反向应用导致了对现有药物的新目标的识别.

结论:

序列到药物概念为SBDD提供了可行的替代方案,特别是对于缺乏3D结构数据的蛋白质.
从蛋白质序列进行端到端微分学习为计算药物设计提供了一个强大的新范式.
这种方法扩大了药物发现的范围,使得以前难以处理的蛋白质成为目标.

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