key: cord-0739627-frxs6nqv
authors: Wang, Jinxian; Zhang, Ying; Nie, Wenjuan; Luo, Yi; Deng, Lei
title: Computational anti-COVID-19 drug design: progress and challenges
date: 2021-11-30
journal: Brief Bioinform
DOI: 10.1093/bib/bbab484
sha: 79229ff32097b3bb8b415593b9dd462675776bdf
doc_id: 739627
cord_uid: frxs6nqv

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

The emerging coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has structural similarities with SARS-CoV-1 and poses a massive crisis to global public health. SARS-CoV-2 can be spread through air droplets, etc. [1] . Due to the high infection rate of COVID-19, a dramatic increase occurred in terms of case and death numbers. As of 10 June 2021, there were 174 061 995 confirmed cases and 3 758 560 deaths, according to the World Health Organization.

Vaccines are considered one of the most effective methods to help human society return to normal. Several vaccines have been authorized for emergency use. Although vaccines have produced very positive effects in many countries, they still face several significant challenges. Studies have reported that a small number of patients presented with venous thrombosis and thrombocytopenia after receiving the first dose of the SARS-CoV-2 vaccine against COVID-19 [2] [3] [4] [5] . Moreover, the vaccine protection effect is not 100%. For example, the efficacy of the two whole-virus inactivated vaccines designed by the China National Biotech Group Company Limited on symptomatic COVID-19 cases was 72.8% and 78.1% in the third phase, separately [6, 7] . The last and most important concern is whether the vaccines are still effective against the emerging SARS-CoV-2 variants. At present, delta has quickly become the dominant SARS-CoV-2 variant. Research suggests that vaccines offer slightly reduced protection against delta, and vaccinated people with breakthrough infections can spread the delta variant [8, 9] . Whether vaccines can effectively slash the spread of delta remains unknown.

Drug development is another important way to defend against viruses. Drug repositioning has been a critical direction in the field of drug research. In recent months, researchers have searched for drugs to treat COVID-19 by finding new therapeutic targets and discovering often unknown relationships among apparently distant diseases. However, most drugs, such as remdesivir, dexamethasone and hydroxychloroquine, fail to display efficacy in treating COVID-19 [10] .

The design of exclusive therapeutic drugs is a trend from the perspective of structural biology and molecular physics [11, 12] . Drug design may have a more therapeutic effect than drug repositioning and drug combinations in the disease of complex, rare or chronic, etc. [13, 14] . Currently, many specific drugs have been designed by virtual screening techniques based on specific pharmacological insights or an end-to-end framework to control the generation of molecules [15] [16] [17] [18] [19] [20] .

With the in-depth study of diseases and exploration of the structure of target proteins [21] [22] [23] [24] [25] , many new technologies have been applied to drug design in extensive practice of medicinal chemistry by improving potency, altering physical properties and eliminating or modifying toxicophores [26] [27] [28] [29] [30] . All drug designs are the interaction between a drug and its target (usually proteins). Therefore, improving methods of predicting the magnitude of protein-ligand interactions can improve the efficiency of drug development [31] [32] [33] [34] . Drug discovery steps require structural optimization of lead compounds to establish the highest possible level of selectivity, potency, and appropriate physicochemical and pharmacokinetic characteristics [26, 35, 36] . Critically, surveying binding hotspots in protein surfaces can help guide the exploration of potential ligand-binding regions [37] [38] [39] [40] [41] .

The world is plagued by the emergence of the SARS-CoV-2 virus. Unfortunately, many methods face data scarcity when designing drugs for new targets [42] . With the development of big data and computer technology, the application of machine learning and deep learning as drug design algorithms has grown in recent decades [43] [44] [45] [46] . Deep learning has become more active in the preparation and process of drug design, such as predicting molecular properties and activity [47, 48] , identifying drug-target interactions [49] [50] [51] and planning chemical syntheses [44, [52] [53] [54] . Therefore, the potential of deep learning and molecular modeling methods helps develop drug design pipelines, especially where there are limited or unavailable target-specific ligand datasets [55, 56] . The designed drug must have an excellent inhibitory effect on the disease. Nevertheless, the prediction of the pharmacokinetics and toxicity characteristics of the scheduled drug can avoid the failure of clinical trials [57, 58] . Now, a set of tools incorporating in silico and deep learning are used to advance sequence-based or structure-based drug design problems in the computeraided drug design area [19, [59] [60] [61] [62] [63] [64] .

Therefore, the rapid application of drug design on an increasingly broader scale with the advancement of biometrics and bioinformatics [65] [66] [67] . Efficient tools are now available for systematically designing compounds with biological activity as preliminary drug candidates [68] [69] [70] . Drug design strategies have been applied to various epidemic diseases [38, [71] [72] [73] [74] . For example, to avoid the early infection of HIV, a generative adversarial autoencoder [75] , which combines a neural network with virtual screening of a chemical database, was developed to design potential HIV-1 entry inhibitors. However, as COVID-19 is raging on a global scale, researchers have  focused their attention on drug design against SARS-COV-2. SARS-CoV-2 is a single-stranded RNA virus that includes two types of proteins: small envelope (E) glycoprotein, structural [spike (S) glycoprotein, membrane (M) glycoprotein, and nucleocapsid (N) protein] and nonstructural (NSP) protein (i.e. nsp1-16), which has a genome size of approximately 30 000 bp [76] . All SARS-CoV-2 proteins play an essential role in pathogenesis and virus replication. For example, S is a promising drug target because it attaches to human cells and participates in entering the cells [77] . NSP is contained in ORF1a and ORF1ab, and they produce two polyproteins, Pp1a and Pp1ab [78, 79] . The latter protein is produced by ribosomal transfer, enabling continuous translation of ORF1a and ORF1ab [80] . Specifically, ORF1ab is the largest protein of SARS-CoV-2, and the ORF1ab gene of human β-coronavirus (HBC) species has a signature of a strong positive selection site in the genome analysis of SARS-CoV-2. The positively selected sites of ORF1ab could justify some clinical features of SARS-CoV-2 compared with other HBCs [81] . Moreover, mutational spectra should be considered when designing drugs [82] . The Pp1a protein contains two viral proteases, 3Clike main protease (Mpro, corresponding to nsp5) and papain-like protease (PLpro, a domain of nsp3) [83] . The main protease (M pro ) of SARS-CoV-2 is a crucial enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription [20, [84] [85] [86] . PL pro is also critical to SARS-CoV-2 replication and represents a promising goal for drug design and development [87] [88] [89] . Among nonstructural proteins, the large, multidomain Nsp3 is encoded by SARS-CoV-2. One of its units is the ADP-ribose phosphatase domain (ADRP; also known as the macro domain, MacroD), which interferes with the host immune response [90] .

It is well known that molecular inhibitors such as drugs can achieve inhibitory effects by targeting cancer or virus expression pathways. For example, seasonal and pandemic influenza have a substantial impact on global public health [91] . A study found that endonuclease activity exists in the independently folded N-terminal domain of PA (PAN) [92, 93] , where PA is a subunit of RNAdependent RNA polymerase (RdRp) that can catalyze viral transcription. Subsequently, the molecular mechanism of the inhibitor was structurally confirmed, and the interaction sites in the crystal structure of the virus strain and the inhibitor were obtained [94] . This information also provides a basis for drug discovery and design. The molecular mechanisms involved in immune regulation during the new coronavirus infection have played a major role in resisting the new coronavirus. Mitogenactivated protein kinase (MAPK) affects cell defense and apoptosis [95] . Azithromycin has been shown to control activation of the MAPK cascade, one of the molecular Figure 1 . Structure-based approaches and AI-based approaches for designing drugs. The decisive part of structure-based and AI-based approaches is the identification of the interaction between molecules and target proteins. Structure-based approaches of designing drugs rely on the three-dimensional structure of the target protein and its active site to identify the interaction between protein and molecule, while AI-based approaches rely on the knowledge of protein and molecule to understand the interaction between them through machine learning or deep learning algorithms. mechanisms involved in virus infection, thereby reducing virus replication [96] .

In general, SARS-CoV-2-related proteins have been suggested as targets for drug design [97] . In particular, the proteins considered in the entire design of anticoronavirus drugs are also related to cancer treatment and other diseases [98] [99] [100] . Indeed, potentially suitable drugs against this virus essentially affect signal transduction and the synthesis of macromolecules, which strongly interfere with the host immune response, particularly the proteins associated with COVID-19 [13] .

Computational drug design approaches applied to COVID-19 can be broadly categorized as (i) structurebased approaches and (ii) AI-based approaches [101] [102] [103] [104] [105] . Some methods consist of both structure-based and AIbased approaches [106] [107] [108] [109] . The drug design process is shown in Figure 1 .

Insertion of halogen atoms on hit or lead compounds has been used to exploit their steric effects because the formation of halogen groups in ligand-target complexes favorably contributes to the stability of the protein-ligand complex [28, 110, 111] . The use of carbamates in medicinal chemistry has increased, and many derivatives are specifically designed to form drugtarget interactions through their carbamate moiety [29, 112, 113] . Fragment-based drug discovery is an effective strategy for generating small-molecule protein inhibitors and drug candidates, which has led to three FDAapproved drugs and clinical trials for nearly 50 molecules [114] . To enable the screening of virtual libraries in search of active compounds, combinatorial chemistry and structure-based design need to be combined, which can exploit ligands and targets' fundamental structural and physicochemical properties [115] [116] [117] . We believe that these approaches have been helpful for designing antiviral compounds.

The genus Coronavirus contains approximately 25 coronaviruses (CoVs), which are essential pathogens causing highly prevalent diseases [38] . By comparing four crystal structures and homologous models representing all three genetic clusters of the genus Coronavirus, it was found that the CoV main proteases (M pro or 3CL pro ) are critical enzymes in viral gene expression and replication and share a highly conserved substrate recognition pocket [118, 119] . In addition, the active sites of Figure 2 . The basic steps of SBDD. First, the preparation of the target macromolecule structure is fundamental to SBDD approaches, which can be obtained by structure identification techniques such as X-ray and NMR analysis, searching the PDB database or using other calculation methods. Next, the binding site of the target macromolecule is identified to determine the protein-ligand interaction, and ligands or drug-like compounds with the limitation of Lipinski's rule of five' are selected from a chemical database to construct a ligand library. When all preparations are ready, molecule docking programs are applied, and scoring functions are mostly used in the post-docking analysis. Finally, the top-ranked molecules are chemically synthesized, and biological evaluation is necessary.

M pro , S1 , S1, S2 and S4 are highly conserved among all coronaviruses [38] . It is possible to design and synthesize inhibitors that target SARS-CoV-2 M pro by analyzing the substrate-binding pocket of SARS-CoV M pro (PDB ID 2H2Z). When designing a new inhibitor, Dai et al. chose an aldehyde as a new warhead to form a covalent bond with cysteine. In addition, they introduced cyclohexyl or 3-fluorophenyl in P2 to enhance the activity and introduced indole groups in P3 to form new hydrogen bonds with S4, which improves the drug-like properties [103] . UCI-1 is a cyclic peptide inhibitor that was designed based on the crystal structure of an inactive SARS-CoV M pro (C145A) variant. The purpose of designing UCI-1 is to mimic the conformation of a C-terminal autolytic cleavage site of the SARS-CoV M pro , a naturally occurring M pro substrate. In UCI-1, the carboxy-terminus of the P2' residue is linked to the amino-terminus of the P2 residue with a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) group, creating a cyclophane. The (2-aminoethyl)phenyl group of AEPA is designed to act as a surrogate for a phenylalanine side chain at position P3' and fill the S3' pocket. Furthermore, research shows that UCI-1 tends to be nontoxic toward human embryonic kidney cells at concentrations that inhibit M pro , but compared with other M pro inhibitors, it shows lower activity [86] .

Owing to the structural elucidation of the target, the application of structure-based drug design (SBDD) software is flourishing [120] [121] [122] ; the basic steps of SBDD are described in detail in Figure 2 . Using different complementary virtual screening and docking approaches can identify nonapproved active compounds as new potential inhibitors of 3CLpro from the ZINC15 library. Then, these compounds could be further optimized by using SBDD [123] . On the other hand, one study started with the Xray crystal structure of SARS-CoV M pro [86] and then used UCSF Chimera software [124] to modify the substrate to create a cyclic peptide inhibitor within the M pro active site. Finally, AutoDock Vina [125] was used to evaluate this model by docking the inhibitor to SARS-CoV-2 M pro .

PL pro aids coronaviruses in their evasion of host innate immune responses because it has the additional function of stripping ubiquitin and interferon-stimulated gene 15 (ISG15) from host-cell proteins [126] . Inhibition of SARS-CoV-2 PL pro with GRL-0617 has three main tasks: impair the virus-induced cytopathogenic effect, maintain the antiviral interferon pathway and reduce viral replication in infected cells [127] . A recent investigation attempted to design potential SARS-CoV PL pro inhibitors containing naphthalene and 3,4-dihydro-2H-pyran moieties connected via-NHCO-linker [89] .

Another study first used HyCoSuL, a novel chemical approach to perform comprehensive activity profiling of SARS-CoV-2 PL pro , and revealed the molecular rules governing PL pro substrate specificity [128] . Then, compared with other proteases, potent inhibitors (VIR250 and VIR251) were designed and biochemically characterized were shown to be more effective against SARS-CoV-2 PL pro and related SARS-CoV-1 PL pro , presenting high selectivity. In addition, it was surprisingly discovered that the P4 amino acids of VIR250 and VIR251 occupy both sides of the wide S4 pocket of SARS-CoV-2 PL pro , which will make contributions to future drug discovery [87] . It is worth noting that there is not enough information about SARS-CoV-2-PL pro , and HyCoSul is based on a hypothesis that SARS-CoV-2-PL pro is highly similar to SARS-CoV-PL pro . The crystal structure of the SARS-CoV-2 ADP-ribose phosphatase domain (ADRP) in multiple states was determined by many studies: in the apo form and in complexes with 2-(N-morpholino) ethane sulfonic acid (MES), ADP-ribose (ADPr) and AMP. Researchers have proposed a robust system to identify potential small-molecule inhibitors with apo crystals diffracting to atomic resolution based on structure-based experiments [90] .

A coronavirus was identified as the causative agent of SARS as early as 2003 [129] . Current research shows that the latest SARS-CoV-2 in 2019 has a very similar structure and function to SARS [130] [131] [132] . The COVID-19 genomes from isolates of China, India, Italy, Nepal and the USA have a sequence similarity of approximately 60% with the human SARS-CoV German isolate, but it has sequence similarity of 79-80% with bat SARS-CoV [133] . Spike glycoprotein (S) is crucial in the attachment of SARS-CoV-2 to host receptors and cell entry, leading to COVID-19 infection [134, 135] . An in silico pharmacophore modeling and virtual screening approach has been used to explore drugs against receptor-binding domain (RBD) of SARS-CoV-2. First, the 3D structure of RBD is modelled, the conservative area is used as a template and Ligand-Scout is used to design the pharmacophore. Then, the Cambridge, DrugBank, ZINC and TIMBLE databases are used to screen lead compounds. Finally, AutoDock Vina Is used to dock the shortlisted lead compounds molecularly and visualize the interacting residues [77] . All the resulting lead compounds are preliminary findings, and clinical and investigational trials are still required. New research points out that the cell membrane receptor angiotensin-converting enzyme 2 (ACE2) plays a key role in the entry of SARS-CoV-2 into cells [136] . SARS-CoV-2 S interacts with ACE2 through the RBD [137] . To this end, a structure-based ACE2 variant dataset was combined with the SARS-CoV-2 RBD, resulting in a total of 242 structural models. These models can be used as a starting point for drug design and be used further to understand the recognition of SARS-CoV-2 S protein by ACE2 [138] . Research has identified a pair of key salt bridges formed by the side chains of K537 and E619. Drugs designed to prevent the formation of these salt bridges can effectively treat COVID-19, but for the simplicity of the protein complex, it is concluded to be applicable to the trimeric S protein [139] .

For the different target proteins of SARS-COV-2, SBDD studies against the COVID-19 pandemic are summarized in Table 1 . In addition, small peptides can be transported across the cell membrane by amino acid and peptide transporters [140] . A variety of small peptide compounds have been developed to inhibit ACE [141, 142] . Another promising method is to select and combine the substructures of highly active compounds to form new peptide analogs [143] [144] [145] [146] .

Designing novel drugs for new diseases is a complicated process necessary to find molecules that bind to specific biomolecular targets and have good physical and chemical properties over a broad chemical space [147] . First, knowledge of the virus and target is necessary. A number of methods have used machine learning or deep learning algorithms to identify the structure of the protein, but most of them could not obtain an accurate structure until AlphaFold [148] appeared. The input of AlphaFold is the given protein sequence, which can be more easily obtained than the structure through experiments. Then, it learns protein-specific potential by training a deep neural network and makes an accurate prediction of the structure by minimizing the potential by gradient descent. Another high-quality prediction of protein structure is C-I-TASSER [149] ; it is an improvement based on I-TASSER [150] , with three modules added on the original basis, which are a new multivariate sequence comparison protocol, an improved meta method NeBcon and an optimized contact potential.

Then, with the ever-increasing number of potential lead compounds derived through virtual screening studies, it is helpful to screen extensive virtual libraries of compounds with improved biological properties such as explicitness and selectivity toward the respective target, lower toxicity or reduced cost to discover drugs with essential biological features. It is laborious for drug designers to extract synthetic information from substantial compound libraries to devise drugs with important biological features; machine learning has become an effective tool to solve this problem [151] [152] [153] [154] [155] . For example, random forest analyses coupled with a unique approach to bioactivity and chemical data curation have led to a series of target-specific and cross-validated predictive feature fingerprints [156] . Moreover, in the study of reference [157] , the desire to map small molecules to Interacting residues were visualized. 

PL pro Macrodomains may help to reduce the viral load and facilitate recovery.

A robust system has been developed to identify potential small-molecule inhibitors for structure-based experiments.

The anomeric C atom.

target interactions across multiple stages of SARS-CoV-2 infection drives the initial target selection. Deep learning can be applied to extract complex and deeper features from simple representations [158] . For example, given a protein pocket and using deep generative modeling for compound design, potential binding compounds can be generated [159] . We know that effective noncovalent inhibitors of the major proteases of SARS-CoV and SARS-CoV-2 should have the same structural and chemical characteristics [84, 160, 161] . The ligand generative adversarial network (LIGANN) is a novel virtual screening technique that is applied for multimodal structure-based ligand design [61] . The ligand is generated in LIGANN to match the shape and chemical properties of the binding pocket and then is decoded into its SMILES sequence, thereby directly realizing AI-based drug design [73] .

CogMol (controlled generation of molecules) is an end-to-end framework that was proposed to design new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity [162] . CogMol combines the variational autoencoder in the molecular SMILES format and the multi-attribute controlled sampling scheme and applies this approach to three SARS-CoV-2 target proteins: the main protease, the RBD of the spike protein and the nonstructural protein. Compared with earlier deep-learning molecular methods, the new approach explores new molecules by adding a meaningful molecular fragment one by one rather than a single atom at a time [163, 164] . However, due to the deviation of the training data or the inaccuracy of the predictor used to control the generation, there may be a certain degree of unreliability and inability to meet the needs of generating molecules with specific properties. ADQN-FBDD [165] is another AI-based drug design approach against SARS-CoV-2. First, an initial molecular database targeting SARS-CoV-2 3CL pro is built by collecting SARS-CoV 3CL pro inhibitors (284 molecules). Then, this set of molecules is split into fragments, and the molecular weight of each fragment does not exceed 200 Daltons. Finally, the features of the fragments are input into the deep Q-learning network to generate potential lead compounds. Most of the computation-aided drug design methods need to be experimentally verified, and ADQN-FBDD is no exception. In conclusion, the major steps of AI-based drug design are described in Figure 3 .

Although vaccines against SARS-CoV-2 have been developed to help prevent COVID-19, there are still some potential problems. Some vaccines developed internationally continue to reveal potential risks and cannot effectively prevent the virus after vaccination. Drug relocation is also a popular way to treat COVID-19, but previous drugs often do not have a significant inhibitory effect on SARS-CoV-2. Therefore, the development of drugs against SARS-CoV-2 is more reasonable and attractive and can provide an effective first line of Figure 3 . The basic steps of AI-based drug design. The first step is target discovery, which means identifying the target protein that interacts with coronavirus. Next, a virtual screening technique is applied to search ligands from chemical databases, and knowledge of target protein-ligand interactions can be obtained from databases or calculated by other calculation methods. Then, AI methods, including machine learning and deep learning, such as generators and predictors, can be used to discover novel drugs. However, before machine learning or deep learning algorithms are used to generate lead molecules, the molecule fed into the learning system should be transformed into vectors, such as molecular descriptors, fingerprints, SMILES strings or grids. defense for emerging coronavirus-related drugs in the future. In recent years, technological developments have enabled the determination of more protein structures and expanded structural biology, thereby accelerating the progress of drug design.

It is estimated that the traditional pharmaceutical industry spends US$2.6 billion to design a new drug, with the failure rate reaching 90% between clinical trials and approval [169] . The emergence of structure-based and AI-based drug design has greatly reduced the cost and time and is more creative. Moreover, the key to structurebased and AI-based drug design is target-protein determination and target-drug interactions. However, SBDD relies more on the three-dimensional structure to optimize the drug compounds, which has a larger chemical space, whereas AI-based structures use machine learning and deep learning algorithms in combination with virtual screening to find the lead molecule. Finally, predicts the binding models between COVID-19 targets and designed molecules by [170] .

We also found that structure-based approaches are more abundant than purely AI-based methods because 

Target2DeNovoDrug https://github.com/bengeof/Target2DeNovoDrug Target2DeNovoDrug offers researchers a fresh set of tools to approach SBDD problems by integrating data science and artificial intelligence (AI).

MolAICal https://molaical.github.io MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining the merits of a deep learning model and classical algorithm.

Target2DeNovoDrugPropMax

https://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax Target2DeNovoDrugPropMax includes a variety of AI methods along with incorporated in silico techniques to provide a holistic tool for automated drug discovery. N/A: N/A means that the tool does not provide a link in their article, due to copyright or other reasons. SBDD methods have been developed for decades. As a result, the entire design-experiment-verification process is f lawless, and the corresponding tools are also more comprehensive. We summarize the tools of drug design in Table 2 . On the other hand, the contribution of AI-based methods to drug design may not achieve the expected results. The possible reason is that these methods are limited to our knowledge. Currently, we do not have enough knowledge to understand a virus that was only discovered less than 2 years ago. Therefore, basic research is essential to the analysis of research applied to biomedicine because basic research is an indispensable foundation for knowledge growth, but it is often undervalued. Nevertheless, powerful and effective calculation methods and pipelines for designing compounds can provide beneficial drug candidates for the treatment of SARS-CoV-2 infection, and these candidates or their variants are likely to produce effective anti-COVID-19 lead drugs. Therefore, we believe that these advances will help make more meaningful contributions to the fight against COVID-19 in the future.

• Drug design is rapidly being applied on an increasingly broader scale with the advancement of biometrics and bioinformatics. In particular, the design of anti-COVID-19 drugs is meaningful work for COIVD-19. • Structure-based approaches use the basic structure, physicochemical properties of ligands and targets to screen virtual libraries to find active anti-COVID-19 drugs. In summary, develop corresponding design strategies of anti-COVID-19 drugs according to the characteristics of different target proteins. • AI-based approaches can extract complex and deeper features from simple drug molecular representations and generate potential binding compounds. It is essential to extract features from compounds or proteins using interpretable methods.

Airborne transmission of sarscov-2: theoretical considerations and available evidence

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Hotspots api: a python package for the detection of small molecule binding hotspots and application to structure-based drug design

Deep learning for a low-data drug design system

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Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

Convolutional embedding of attributed molecular graphs for physical property prediction

Protein-ligand scoring with convolutional neural networks

Deepsite: proteinbinding site predictor using 3d-convolutional neural networks

Atomic convolutional networks for predicting protein-ligand binding affinity

Retrosynthetic reaction prediction using neural sequence-to-sequence models

Neural-symbolic machine learning for retrosynthesis and reaction prediction

Predicting organic reaction outcomes with Weisfeiler-Lehman network

Openchem: a deep learning toolkit for computational chemistry and drug design

Accelerating de novo drug design against novel proteins using deep learning

In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts

Deep learning for molecular generation

Deep reinforcement learning for de novo drug design

Guacamol: benchmarking models for de novo molecular design

From target to drug: generative modeling for the multimodal structure-based ligand design

Computational anti-COVID-19 drug design: progress and challenges | 11

Target-specific drug design method combining deep learning and water pharmacophore

Target2denovodrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest

Molaical: a soft tool for 3d drug design of protein targets by artificial intelligence and classical algorithm

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Reconsidering anion inhibitors in the general context of drug design studies of modulators of activity of the classical enzyme carbonic anhydrase

Computer-aided drug design of β-secretase, γ -secretase and anti-tau inhibitors for the discovery of novel Alzheimer's therapeutics

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Identification of potential binders of the main protease 3clpro of the covid-19 via structure-based ligand design and molecular modeling

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Development of a neural network-based approach for prediction of potential hiv-1 entry inhibitors using deep learning and molecular modeling methods

Grp78: a cell's response to stress

Computer-aided drug design against spike glycoprotein of sars-cov-2 to aid covid-19 treatment

Origin and evolution of pathogenic coronaviruses

Structural and functional conservation of the programmed-1 ribosomal frameshift signal of SARS coronavirus 2 (SARS-cov-2)

The primary structure and expression of the second open reading frame of the polymerase gene of the coronavirus mhv-a59; a highly conserved polymerase is expressed by an efficient ribosomal frameshifting mechanism

Positive selection as a key player for sars-cov-2 pathogenicity: insights into orf1ab, s and e genes

Mutational spectra of sars-cov-2 orf1ab polyprotein and signature mutations in the United States of America

The nonstructural proteins directing coronavirus RNA synthesis and processing

The crystal structures of severe acute respiratory syndrome virus main protease and its complex with an inhibitor

Structure of m pro from sars-cov-2 and discovery of its inhibitors

Structurebased design of a cyclic peptide inhibitor of the sars-cov-2 main protease

Activity profiling and crystal structures of inhibitor-bound sars-cov-2 papain-like protease: a framework for anti-covid-19 drug design

Chemical-informatics approach to covid-19 drug discovery: Monte Carlo based qsar, virtual screening and molecular docking study of some inhouse molecules as papain-like protease (plpro) inhibitors

Structure-based drug designing of naphthalene based SARS-cov plpro inhibitors for the treatment of covid-19

Crystal structures of sars-cov-2 ADP-ribose phosphatase: from the apo form to ligand complexes

The 1918 Spanish influenza: integrating history and biology

Inhibition of cap (m7gpppxm)-dependent endonuclease of influenza virus by 4-substituted 2, 4-dioxobutanoic acid compounds

The cap-snatching endonuclease of influenza virus polymerase resides in the pa subunit

Structural analysis of specific metal chelating inhibitor binding to the endonuclease domain of influenza ph1n1

Molecular insights into the mapk cascade during viral infection: potential crosstalk between hcq and hcq analogues

Role of azithromycin in antiviral treatment: enhancement of interferon-dependent antiviral pathways and mitigation of inflammation may rely on inhibition of the mapk cascade?

Covid-19 pathophysiology: a review

Choline kinase inhibitors as a novel approach for antiproliferative drug design

Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin

Carbonic anhydrase inhibitors drug design. Carbonic anhydrase: mechanism, regulation, links to disease, and industrial applications

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase

Crystallographic and electrophilic fragment screening of the sars-cov-2 main protease

Structure-based design of antiviral drug candidates targeting the sars-cov-2 main protease

Multi-target dopamine d3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning

Advances in de novo drug design: From conventional to machine learning methods

Deep learning for molecular design-a review of the state of the art

Mind and machine in drug design

Tar-get2denovodrugpropmax: a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

Halogen bonding a novel interaction for rational drug design

Halogen bonding for rational drug design and new drug discovery

Role of organic carbamates in anticancer drug design

Carbamate and n-pyrimidine mitigate amide hydrolysis: Structure-based drug design of tetrahydroquinoline ido1 inhibitors

Fragment linking strategies for structure-based drug design

Thiourea derivatives in drug design and medicinal chemistry: a short review

Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches

Computational bioactivity fingerprint similarities to navigate the discovery of novel scaffolds

Identification of novel inhibitors of the SARS coronavirus main protease 3clpro

Synthesis and evaluation of keto-glutamine analogues as potent inhibitors of severe acute respiratory syndrome 3clpro

Software for structure-based drug design

Drug guru: a computer software program for drug design using medicinal chemistry rules

Emerging need of today: significant utilization of various databases and softwares in drug design and development

Repurposing approved drugs as potential inhibitors of 3cl-protease of sars-cov-2: virtual screening and structure based drug design

Ucsf chimerax: structure visualization for researchers, educators, and developers

Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Identification of severe acute respiratory syndrome coronavirus replicase products and characterization of papain-like protease activity

Papain-like protease regulates sars-cov-2 viral spread and innate immunity

Synthesis of a hycosul peptide substrate library to dissect protease substrate specificity

A major outbreak of severe acute respiratory syndrome in Hong Kong

Coagulation disorders in coronavirus infected patients: Covid-19, sars-cov-1, mers-cov and lessons from the past

Aerosol and surface stability of sars-cov-2 as compared with sars-cov-1

SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis. The lancet microbe

Corona virus (covid19) genome: genomic and biochemical analysis revealed its possible synthetic origin

Mechanisms of coronavirus cell entry mediated by the viral spike protein

Structure, function, and antigenicity of the sars-cov-2 spike glycoprotein

Sars-cov-2 cell entry depends on ace2 and tmprss2 and is blocked by a clinically proven protease inhibitor

Characterization of the receptorbinding domain (rbd) of 2019 novel coronavirus: implication for development of rbd protein as a viral attachment inhibitor and vaccine

Structural models of human ace2 variants with sars-cov-2 spike protein for structure-based drug design

Antiviral drug design based on the opening mechanism of spike glycoprotein in sars-cov-2

Delivery aspects of small peptides and substrates for peptide transporters

Angiotensin converting enzyme inhibitors: 1-glutarylindoline-2-carboxylic acid derivatives

Structural specificity of mucosal-cell transport and metabolism of peptide drugs: implication for oral peptide drug delivery

Rational design of small modified peptides as ace inhibitors

Novel anti-hiv cyclotriazadisulfonamide derivatives as modeled by ligand-and receptor-based approaches

In silico prediction of novel phosphodiesterase type-5 inhibitors derived from sildenafil, vardenafil and tadalafil

Qsar and docking studies of novel antileishmanial diaryl sulfides and sulfonamides

Drug design strategies to avoid resistance in direct-acting antivirals and beyond

Improved protein structure prediction using potentials from deep learning

Deep-learning contactmap guided protein structure prediction in casp13

I-tasser server for protein 3d structure prediction

Drug design by machine learning: support vector machines for pharmaceutical data analysis

Machine learning in chemoinformatics and drug discovery

toxpred: a machine learningbased approach to estimate the toxicity of drug candidates

Machine learning approaches to rational drug design

Applications of machine learning and artificial intelligence for covid-19 (sars-cov-2) pandemic: a review

Novel development of predictive feature fingerprints to identify chemistry-based features for the effective drug design of sars-cov-2 target antagonists and inhibitors using machine learning

A sars-cov-2 protein interaction map reveals targets for drug repurposing

Smiles-based deep generative scaffold decorator for de-novo drug design

Deep learning for drug design: modeling molecular shapes

Playmolecule proteinprepare: a web application for protein preparation for molecular dynamics simulations

Potential covid-2019 3c-like protease inhibitors designed using generative deep learning approaches

Cogmol: targetspecific and selective drug design for covid-19 using deep generative models

Junction tree variational autoencoder for molecular graph generation

Graph convolutional policy network for goal-directed molecular graph generation

Ai-aided design of novel targeted covalent inhibitors against sars-cov-2

Deepscaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning

Cov_fb3d: a de novo covalent drug design protocol integrating the ba-samp strategy and machinelearning-based synthetic tractability evaluation

QPoweredTar-get2DeNovoDrugPropMax: a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

From chemoinformatics to deep learning: an open road to drug discovery

Covid-19 docking server: a meta server for docking small molecules, peptides and antibodies against potential targets of covid-19

Persistent spectral based machine learning (PerSpect ML) for drug design