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Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery

文献类型：期刊论文

作者Zhang, Haiping1; Lin, Xiao2; Wei, Yanjie1; Zhang, Huiling1; Liao, Linbu3; Wu, Hao1; Pan, Yi1; Wu, Xuli2
刊名FRONTIERS IN MOLECULAR BIOSCIENCES
出版日期2022-06-01
卷号9页码:15
关键词extremely large-scale virtual screening deep learning DFCNN Trypsin I Protease de novo drug screening
DOI10.3389/fmolb.2022.872086
英文摘要Computational methods with affordable computational resources are highly desirable for identifying active drug leads from millions of compounds. This requires a model that is both highly efficient and relatively accurate, which cannot be achieved by most of the current methods. In real virtual screening (VS) application scenarios, the desired method should perform much better in selecting active compounds by prediction than by random chance. Here, we systematically evaluate the performance of our previously developed DFCNN model in large-scale virtual screening, and the results show our method has approximately 22 times the success rate compared to the random chance on average with a score cutoff of 0.99. Of the 102 test cases, 10 cases have more than 98 times the success rate of a random guess. Interestingly, in three cases, the prediction success rate is 99 times that of a random guess by a score cutoff of 0.99. This indicates that in most situations after our extremely large-scale VS, the dataset can be reduced 20 to 100 times for the next step of virtual screening based on docking or MD simulation. Furthermore, we have employed an experimental method to verify our computational method by finding several activity inhibitors for Trypsin I Protease. In addition, we also show its proof-of-concept application in de novo drug screening. The results indicate the massive potential of this method in the first step of the real drug development workflow. Moreover, DFCNN only takes about 0.0000225s for one protein-compound prediction on average with 80 Intel CPU cores (2.00 GHz) and 60 GB RAM, which is at least tens of thousands of times faster than AutoDock Vina or Schrodinger high-throughput virtual screening. Additionally, an online webserver based on DFCNN for large-scale screening is available at for the convenience of the users.
资助项目Key-Area Research and Development Program of Guangdong Province[2019B020213001] ; National Science Foundation for Young Scientists of China[62106253] ; Research Funding for Innovation Project of Universities in Guangdong Province[2018KTSCX192] ; Shenzhen KQTD Project[U1813203] ; National Science Foundation of China[U1813203] ; Shenzhen Basic Research Fund[RCYX2020071411473419] ; CAS Key Lab[2011DP173015] ; Research Funding of Shenzhen[JCYJ201803053000708] ; Strategic Priority CAS Project[XDB38000000]
WOS研究方向Biochemistry & Molecular Biology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000812040200001
源URL[http://119.78.100.204/handle/2XEOYT63/19620]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Haiping; Pan, Yi; Wu, Xuli
作者单位1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Joint Engn Res Ctr Hlth Big Data Intelligent Anal, Shenzhen, Peoples R China
2.Shenzhen Univ, Sch Med, Shenzhen, Peoples R China
3.Zhejiang Univ, Coll Software Technol, Hangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Haiping,Lin, Xiao,Wei, Yanjie,et al. Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery[J]. FRONTIERS IN MOLECULAR BIOSCIENCES,2022,9:15.
APA Zhang, Haiping.,Lin, Xiao.,Wei, Yanjie.,Zhang, Huiling.,Liao, Linbu.,...&Wu, Xuli.(2022).Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery.FRONTIERS IN MOLECULAR BIOSCIENCES,9,15.
MLA Zhang, Haiping,et al."Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery".FRONTIERS IN MOLECULAR BIOSCIENCES 9(2022):15.

入库方式： OAI收割

来源：计算技术研究所

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