Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

doi:10.1016/j.cjche.2024.01.021

中国化学工程学报 ›› 2024, Vol. 72 ›› Issue (8): 85-94.DOI: 10.1016/j.cjche.2024.01.021

Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

Yujing Zhao¹, Qilei Liu^1,2, Jian Du¹, Qingwei Meng^1,2, Liang Sun^3,4, Lei Zhang¹

1 State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China;
2 Ningbo Institute of Dalian University of Technology, Ningbo 315016, China;
3 Shenzhen Shuli Tech Co., Ltd, Shenzhen 518126, China;
4 Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China

收稿日期:2023-12-01 修回日期:2024-01-23 出版日期:2024-08-28 发布日期:2024-10-17
通讯作者: Lei Zhang,E-mail:keleiz@dlut.edu.cn
基金资助:
The authors are grateful for financial supports of the National Natural Science Foundation of China (22078041, 22278053, 22208042), Dalian High-level Talents Innovation Support Program (2023RQ059) and “the Fundamental Research Funds for the Central Universities (DUT20JC41, DUT22YG218)”.

Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

Yujing Zhao¹, Qilei Liu^1,2, Jian Du¹, Qingwei Meng^1,2, Liang Sun^3,4, Lei Zhang¹

1 State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China;
2 Ningbo Institute of Dalian University of Technology, Ningbo 315016, China;
3 Shenzhen Shuli Tech Co., Ltd, Shenzhen 518126, China;
4 Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China

Received:2023-12-01 Revised:2024-01-23 Online:2024-08-28 Published:2024-10-17
Contact: Lei Zhang,E-mail:keleiz@dlut.edu.cn
Supported by:
The authors are grateful for financial supports of the National Natural Science Foundation of China (22078041, 22278053, 22208042), Dalian High-level Talents Innovation Support Program (2023RQ059) and “the Fundamental Research Funds for the Central Universities (DUT20JC41, DUT22YG218)”.

摘要/Abstract

摘要： Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, DrugCAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking, binding pose metadynamics (BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859 shows potential Factor Xa inhibition, with an IC₅₀ value of 34.57 μmol·L^-1. Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

关键词: Chemical product design, Mathematical programming method, Deep learning, Binding affinity, Factor Xa inhibitor

Abstract: Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, DrugCAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking, binding pose metadynamics (BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859 shows potential Factor Xa inhibition, with an IC₅₀ value of 34.57 μmol·L^-1. Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

Key words: Chemical product design, Mathematical programming method, Deep learning, Binding affinity, Factor Xa inhibitor

Yujing Zhao, Qilei Liu, Jian Du, Qingwei Meng, Liang Sun, Lei Zhang. Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline[J]. 中国化学工程学报, 2024, 72(8): 85-94.

Yujing Zhao, Qilei Liu, Jian Du, Qingwei Meng, Liang Sun, Lei Zhang. Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline[J]. Chinese Journal of Chemical Engineering, 2024, 72(8): 85-94.

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Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

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