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

doi:10.3389/fmolb.2022.872086

	Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery
	Zhang, Haiping 1; Lin, Xiao 2; Wei, Yanjie 1; Zhang, Huiling 1; Liao, Linbu 3; Wu, Hao 1; Pan, Yi 1; Wu, Xuli 2
	2022-06-01
发表期刊	FRONTIERS IN MOLECULAR BIOSCIENCES
卷号	9 页码:15
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.
关键词	extremely large-scale virtual screening deep learning DFCNN Trypsin I Protease de novo drug screening
DOI	10.3389/fmolb.2022.872086
收录类别	SCI
语种	英语
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
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000812040200001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19621
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.