Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

Abstract

A novel coronavirus called 2019-nCoV was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. 2019-nCoV spreads more rapidly than SARS-CoV. Unfortunately, there is no drug to combat the virus. It is of high significance to develop a drug that can combat the virus effectively before the situation gets worse. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In this paper, we first collected virus RNA sequences from the GISAID database, translated the RNA sequences into protein sequences, and built a protein 3D model using homology modeling. Coronavirus main protease is considered to be a major therapeutic target, thus this paper focused on drug screening based on the modeled 2019-nCov_main_protease structure. The deep learning based method DFCNN, developed by our group, can identify/rank the protein-ligand interactions with relatively high accuracy. DFCNN is capable of performing virtual screening quickly since no docking or molecular dynamic simulation is needed. DFCNN identifies potential drugs for 2019-nCoV protease by performing drug screening against 4 chemical compound databases. Also, we performed drug screening for all tripeptides against the binding site of 2019-nCov_main_protease since peptides often show better stability, more bio-availability and negligible immune responses. In the end, we provided the list of possible chemical ligands and peptide drugs for experimental validation.

Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

“…However, previously reported docking (virtual screening) campaigns with Mpro targets were able to process only few millions or even thousands compounds. [6,[28][29][30] The main reason for that is that conventional docking is too computationally expensive and slow, while the libraries of available chemicals are growing exponentially. [31] To address this general challenge, we have recently developed a novel deep learning-based approach for accelerated screening of large chemical libraries, consisting of billions of entities.…”

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management

“…In this direction, a deep-learning-based approach was investigated by Zhang et al for screening available drugs for effective treatment of COVID-19. 96 In this model (DFCNN), RNA sequences were collected from the GISAID database to explore related 3D protein sequences modeling using homology modeling. The DFCNN explores possible protein–ligand interactions of high accuracy to perform drug screening without using docking or molecular dynamics.…”

“…This protease-based modeling successfully identified 4 chemical compound databases and confirmed that peptides-based drugs exhibited good stability, the desired bioavailability, and negligible immune responses. 96 …”

Déjà vu: Stimulating open drug discovery for SARS-CoV-2

“…In the space of a few weeks after the acknowledgement of the latest coronavirus outbreak, several laboratories started to deposit homology models for the main viral protease [42,43], and then for all the SARS-CoV-2 proteins using the server I-TASSER [44]. Others performed docking in these models [45] or developed their own models to understand how the virus enters cells by modeling the Spike (S) protein and human angiotensin-converting enzyme 2 (ACE2) protein interaction [46]. At the time of writing there were structures available for the SARS-CoV-2 main protease in complex with an inhibitor N3 (PDB ID 6LU7) [47] and S1 and S2 subunits of Spike glycoprotein receptor-binding domain up (6VSB), the post fusion core subunit (6LXT) and the HR2 domain (6LVN) [48,49].…”