首页|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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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 li-braries.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 bio-logical assays.Consequently,EN300-331859 is identified as a novel and effective sub-micromolar in-hibitor of Factor Xa.

Chemical product designMathematical programming methodDeep learningBinding affinityFactor Xa inhibitor

Yujing Zhao、Qilei Liu、Jian Du、Qingwei Meng、Liang Sun、Lei Zhang

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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

Ningbo Institute of Dalian University of Technology,Ningbo 315016,China

Shenzhen Shuli Tech Co.,Ltd,Shenzhen 518126,China

Department of Physics,City University of Hong Kong Tat Chee Avenue,Kowloon,Hong Kong SAR,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaDalian Highlevel Talents Innovation Support ProgramFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities

2207804122278053222080422023RQ059DUT20JC41DUT22YG218

2024

10.1016/j.cjche.2024.01.021

中国化学工程学报(英文版)
中国化工学会

中国化学工程学报(英文版)

CSTPCDEI
影响因子：0.818
ISSN：1004-9541
年,卷(期)：2024.72(8)