Sequence-based drug design as a concept in computational drug design

Lifan Chen, Zisheng Fan, Jie Chang, Ruirui Yang, Hui Hou, Hao Guo, Yinghui Zhang, Tianbiao Yang, Chenmao Zhou, Qibang Sui, Zhengyang Chen, Chen Zheng, Xinyue Hao, Keke Zhang, Rongrong Cui, Zehong Zhang, Hudson Ma, Yiluan Ding, Naixia Zhang, Xiaojie Lu, Xiaomin Luo, Hualiang Jiang, Sulin Zhang () and Mingyue Zheng ()
Additional contact information
Lifan Chen: Chinese Academy of Sciences
Zisheng Fan: Chinese Academy of Sciences
Jie Chang: Chinese Academy of Sciences
Ruirui Yang: Chinese Academy of Sciences
Hui Hou: Chinese Academy of Sciences
Hao Guo: Chinese Academy of Sciences
Yinghui Zhang: Chinese Academy of Sciences
Tianbiao Yang: Chinese Academy of Sciences
Chenmao Zhou: Chinese Academy of Sciences
Qibang Sui: Chinese Academy of Sciences
Zhengyang Chen: Chinese Academy of Sciences
Chen Zheng: Chinese Academy of Sciences
Xinyue Hao: Chinese Academy of Sciences
Keke Zhang: Chinese Academy of Sciences
Rongrong Cui: Chinese Academy of Sciences
Zehong Zhang: Chinese Academy of Sciences
Hudson Ma: Chinese Academy of Sciences
Yiluan Ding: Chinese Academy of Sciences
Naixia Zhang: Chinese Academy of Sciences
Xiaojie Lu: Chinese Academy of Sciences
Xiaomin Luo: Chinese Academy of Sciences
Hualiang Jiang: Chinese Academy of Sciences
Sulin Zhang: Chinese Academy of Sciences
Mingyue Zheng: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-21

Abstract: Abstract Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

Date: 2023
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DOI: 10.1038/s41467-023-39856-w

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