AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Abstract

The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CL pro or M pro ) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CL pro . We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.author/funder. All rights reserved. No reuse allowed without permission. : bioRxiv preprint disease (COVID-19) worldwide. 1 As of March 2, 2020, more than 90,000 people have been infected by SARS-CoV-2 and more than 3000 people have been reported dead according to Johns Hopkins Coronavirus map tracker. 2 The numbers of infection and death are still increasing. To face the considerable threat of SARS-CoV-2, it is urgent to develop new inhibitors or drugs against this deadly virus. Unfortunately, since the outbreak of severe acute respiratory syndrome (SARS) eighteen years ago, there has been no approved treatment against the SARS coronavirus (SARS-CoV), 3 which is similar to SARS-CoV-2. Repurposing potential drugs such as lopinavir and ritonavir also failed to SARS-CoV-2 injected patients. 4 Structure-based antiviral drug design with a new artificial intelligence algorithm may represent a more helpful approach to get the SARS-CoV-2 targeted inhibitors or drugs. Thanks to the prompt efforts of many researchers, we have several pieces of important information about this vital virus genome and protein structures. We now know that the non-structural protein 5 (Nsp5) is the main protease (M pro ) of SARS-CoV-2 and it is a cysteine protease, which also been called "3C-like protease" (3CL pro ). Moreover, we know that the 3D structure of 3CL pro is very similar to SARS-CoV with a sequence identity of >96% and 3D structure superposition RMSDCα of 0.44 Å as shown in Figures S1 and S2. 3CL pro has been reported as an attractive target for developing anti-coronaviral drugs: 1) this protease is highly conserved in both sequences and 3D structures; 5 2) 3CL pro is a key enzyme for related virus (including SARS and SARS-CoV-2) replication; 3) it only exists in the virus, not in humans. Developing specific antiviral drugs targeting 3CL pro of the specific virus has shown significant success; for example, both approved drugs lopinavir and ritonavir can completely occupy the substrate-binding site of 3CL pro to break down the replication of human immunodeficien...

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

“…In the field of epidemiological modeling and medicines, deep learning is used to explore and analyze the protein structure of the virus to identify the essential components for the vaccine [ 36 ]. For the development of effective drugs, the systems demonstrated in [ 39 ] trained GAs and GANs, [ 41 ] used reinforcement learning techniques, and [ 43 ] applied LSTM networks. The human-infecting virus can be identified using deep learning-based architectures utilizing its next-generation sequence shown in [ 45 ].…”

“…Moreover, the author ensured that the detected candidate molecules are different from the existing compound. Tang et al [ 41 ] used reinforcement learning techniques to discover the compounds that inhibit COVID-19. The system generated 284 molecules and broke down the protein into 316 fragments, which later combined using a deep Q-learning network to design a fragment-based drug.…”

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

“…Based on the results, they provide a list of potential inhibitors that can help facilitate drug development for COVID-19. Tang et al [185] propose the use of reinforcement learning (RL) models to predict potential lead compounds targeting SARS-CoV-2. Similarly, in [186] the authors propose a collaborative and open antiviral discovery approach using deep RL technique to discover new molecules to fight COVID-19.…”

De Novo Design and Bioactivity Prediction of SARS-CoV-2 Main Protease Inhibitors Using ULMFit

“…For fine-tuning, the dataset consisted of over 280K molecules screened against SARS-CoV-1 M pro available on PubChem (AID: 1706). Originally, AID1706 consisted of 405 active molecules, but we augmented it with 224 inhibitors collected from literature by Tang et al, ( https://github.com/tbwxmu/2019-nCov ) [28] . In total, our fine-tuning dataset was highly unbalanced, with 629 active molecules and 288,940 inactive ones.…”

“…One of the most used architectures for this are recurrent neural networks (RNN). RNN's are neural networks that can deal with sequences of variable length, such as the ones in natural language processing27 , audio28 and video29 tasks. The recurrent operation is the heart of RNN; each item in a sequence serves as input to the neural net in order to predict the next item in the sequence30,31 .…”