1
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Das S, Nath S, Shahjahan, Dey SK. Plausible mechanism of drug resistance and side-effects of COVID-19 therapeutics: a bottleneck for its eradication. Daru 2024:10.1007/s40199-024-00524-z. [PMID: 39026019 DOI: 10.1007/s40199-024-00524-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 06/11/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND COVID-19 pandemic has turned our world upside down by meddling with our normal lives. While there is no definitive drug against SARS-CoV-2, antiviral drugs that are already in the market, are being repurposed against it, could now complete long-term as well as all age-specific investigations, and they are successful in saving millions of lives. Nevertheless, side-effects are emergingly seen in the patients undergoing treatment, and ineffectiveness is increasingly found due to the emerging notorious variants of the virus. Many of them are also facing serious co-infections including black fungus, Zika, and H1N1 virus to name a few. OBJECTIVES Therefore, this review highlights both drug resistance, their side-effects, and the significance for proper and long-term clinical trials of all age groups including children. METHODS We have explored and proposed the mechanisms of drug resistance that may arise due to the misuse or overuse of drugs based on available experimental reports. RESULTS The review provides solutions to the aforesaid issues of drug-resistance and side-effects by providing combination therapies, ancillary treatments, and other preventive strategies that can be useful in preventing drawbacks thereby curbing COVID-19 or similar future infections to maintain our normal lives. CONCLUSION COVID-19 and its long-term effects, if any, can be eradicated with strategic and mindful use of related therapeutics in a controlled manner.
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Affiliation(s)
- Swarnali Das
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology, Kanpur, 208016, India
| | - Sreyashi Nath
- Imaging Cell Signaling and Therapeutics Lab, Advanced Centre for Training Research and Education in Cancer, Navi Mumbai, 410210, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
| | - Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.
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2
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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3
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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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4
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Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 2024; 22:411. [PMID: 38702711 PMCID: PMC11069149 DOI: 10.1186/s12967-024-05067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/06/2024] Open
Abstract
Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.
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Affiliation(s)
- Claudio Carini
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, New Hunt's House, King's College London, Guy's Campus, London, UK.
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD, USA.
| | - Attila A Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI, USA.
- Legorreta Cancer Center at Brown University, Providence, RI, USA.
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5
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Humayun F, Khan F, Khan A, Alshammari A, Ji J, Farhan A, Fawad N, Alam W, Ali A, Wei DQ. De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations. J Biomol Struct Dyn 2024; 42:3019-3029. [PMID: 37449757 DOI: 10.1080/07391102.2023.2234481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/30/2023] [Indexed: 07/18/2023]
Abstract
De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fahad Humayun
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Fatima Khan
- National Institute of Health, Islamabad, Pakistan
| | - Abbas Khan
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Jun Ji
- Henan Provincial Engineering and Technology Center of Health Products for Livestock and Poultry, Henan Provincial Engineering and Technology Center of Animal Disease Diagnosis and Integrated Control, Nanyang Normal University, Nanyang, PR China
| | - Ali Farhan
- Department of Chemistry, Chung Yuan Christian University, Taoyuan, Taiwan
| | - Nasim Fawad
- Poultry Research Institute, Rawalpindi, Pakistan
| | - Waheed Alam
- National Institute of Health, Islamabad, Pakistan
| | - Arif Ali
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- Centre for Research in Molecular Modeling, Concordia University, Québec, Canada
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6
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Izmailyan R, Matevosyan M, Khachatryan H, Shavina A, Gevorgyan S, Ghazaryan A, Tirosyan I, Gabrielyan Y, Ayvazyan M, Martirosyan B, Harutyunyan V, Zakaryan H. Discovery of new antiviral agents through artificial intelligence: In vitro and in vivo results. Antiviral Res 2024; 222:105818. [PMID: 38280564 DOI: 10.1016/j.antiviral.2024.105818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
In this research, we employed a deep reinforcement learning (RL)-based molecule design platform to generate a diverse set of compounds targeting the neuraminidase (NA) of influenza A and B viruses. A total of 60,291 compounds were generated, of which 86.5 % displayed superior physicochemical properties compared to oseltamivir. After narrowing down the selection through computational filters, nine compounds with non-sialic acid-like structures were selected for in vitro experiments. We identified two compounds, DS-22-inf-009 and DS-22-inf-021 that effectively inhibited the NAs of both influenza A and B viruses (IAV and IBV), including H275Y mutant strains at low micromolar concentrations. Molecular dynamics simulations revealed a similar pattern of interaction with amino acid residues as oseltamivir. In cell-based assays, DS-22-inf-009 and DS-22-inf-021 inhibited IAV and IBV in a dose-dependent manner with EC50 values ranging from 0.29 μM to 2.31 μM. Furthermore, animal experiments showed that both DS-22-inf-009 and DS-22-inf-021 exerted antiviral activity in mice, conferring 65 % and 85 % protection from IAV (H1N1 pdm09), and 65 % and 100 % protection from IBV (Yamagata lineage), respectively. Thus, these findings demonstrate the potential of RL to generate compounds with promising antiviral properties.
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Affiliation(s)
- Roza Izmailyan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia
| | | | - Hamlet Khachatryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Anastasiya Shavina
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Smbat Gevorgyan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Artur Ghazaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia
| | | | | | | | | | | | - Hovakim Zakaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia.
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7
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Haghir Ebrahim Abadi MH, Ghasemlou A, Bayani F, Sefidbakht Y, Vosough M, Mozaffari-Jovin S, Uversky VN. AI-driven covalent drug design strategies targeting main protease (m pro) against SARS-CoV-2: structural insights and molecular mechanisms. J Biomol Struct Dyn 2024:1-29. [PMID: 38287509 DOI: 10.1080/07391102.2024.2308769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | - Fatemeh Bayani
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Sina Mozaffari-Jovin
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vladimir N Uversky
- Department of Molecular Medicine, University of South Florida, Tampa, FL, USA
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8
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Jiang H, Zou X, Zhou X, Zhang J, Li J. Crystal structure of SARS-CoV-2 main protease (M pro) mutants in complex with the non-covalent inhibitor CCF0058981. Biochem Biophys Res Commun 2024; 692:149352. [PMID: 38056159 DOI: 10.1016/j.bbrc.2023.149352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
SARS-CoV-2 constantly circulates and evolves worldwide, generating many variants and posing a menace to global health. It is urgently needed to discover effective medicines to treat the disease caused by SARS-CoV-2 and its variants. An established target for anti-SARS-CoV-2 drug discovery is the main protease (Mpro), since it exerts an irreplaceable action in viral life cycle. CCF0058981, derived from ML300, is a non-covalent inhibitor that exhibits low nanomolar potency against SARS-CoV-2 Mpro and submicromolar anti-SARS-CoV-2 activity, thereby providing a valuable starting point for drug design. However, structural basis underlying inhibition of SARS-CoV-2 Mpro by CCF0058981 remains undetermined. In this study, the crystal structures of CCF0058981 in complex with two SARS-CoV-2 Mpro mutants (M49I and V186F), which have been identified in the recently emerged Omicron subvariants, were solved. Structural analysis defined the pivotal molecular factors responsible for the interactions between CCF0058981 and these two Mpro mutants, and revealed the binding modes of CCF0058981 to Mpro M49I and V186F mutants. These data not only provide structural insights for SARS-CoV-2 Mpro inhibition by CCF0058981, but also add to develop effective broad-spectrum drugs against SARS-CoV-2 as well as its variants.
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Affiliation(s)
- Haihai Jiang
- School of Basic Medical Sciences, Nanchang University, Nanchang, 330031, China
| | - Xiaofang Zou
- College of Pharmaceutical Sciences, Gannan Medical University, Ganzhou, 341000, China
| | - Xuelan Zhou
- Jiangxi Jmerry Biopharmaceutical Co., Ltd., Ganzhou, 341000, China; Shenzhen Crystalo Biopharmaceutical Co., Ltd., Shenzhen, 518118, China
| | - Jin Zhang
- School of Basic Medical Sciences, Nanchang University, Nanchang, 330031, China.
| | - Jian Li
- College of Pharmaceutical Sciences, Gannan Medical University, Ganzhou, 341000, China.
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9
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Matevosyan M, Harutyunyan V, Abelyan N, Khachatryan H, Tirosyan I, Gabrielyan Y, Sahakyan V, Gevorgyan S, Arakelov V, Arakelov G, Zakaryan H. Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning. J Biomol Struct Dyn 2023; 41:10798-10812. [PMID: 36541127 DOI: 10.1080/07391102.2022.2158936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/10/2022] [Indexed: 12/24/2022]
Abstract
Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vahram Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Grigor Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Hovakim Zakaryan
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
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10
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Choi K, Lee Y, Kim C. An In Silico Study for Expanding the Utility of Cannabidiol in Alzheimer's Disease Therapeutic Development. Int J Mol Sci 2023; 24:16013. [PMID: 37959001 PMCID: PMC10648567 DOI: 10.3390/ijms242116013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023] Open
Abstract
Cannabidiol (CBD), a major non-psychoactive component of the cannabis plant, has shown therapeutic potential in Alzheimer's disease (AD). In this study, we identified potential CBD targets associated with AD using a drug-target binding affinity prediction model and generated CBD analogs using a genetic algorithm combined with a molecular docking system. As a result, we identified six targets associated with AD: Endothelial NOS (ENOS), Myeloperoxidase (MPO), Apolipoprotein E (APOE), Amyloid-beta precursor protein (APP), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), and Presenilin-1 (PSEN1). Furthermore, we generated CBD analogs for each target that optimize for all desired drug-likeness properties and physicochemical property filters, resulting in improved pIC50 values and docking scores compared to CBD. Molecular dynamics (MD) simulations were applied to analyze each target's CBD and highest-scoring CBD analogs. The MD simulations revealed that the complexes of ENOS, MPO, and ADAM10 with CBD exhibited high conformational stability, and the APP and PSEN1 complexes with CBD analogs demonstrated even higher conformational stability and lower interaction energy compared to APP and PSEN1 complexes with CBD. These findings demonstrated the capable binding of the six identified targets with CBD and the enhanced binding stability achieved with the developed CBD analogs for each target.
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Affiliation(s)
- Kyudam Choi
- Heerae Co., Ltd., Seoul 06253, Republic of Korea;
| | - Yurim Lee
- Department of Software, Sejong University, Seoul 05006, Republic of Korea;
| | - Cheongwon Kim
- Department of Software, Sejong University, Seoul 05006, Republic of Korea;
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11
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Hongyu H, Wu T, He F, Chao M, Huang J, Wang X, Niu Z, Tang B. The binding mechanism of failed, in processing and succeed inhibitors target SARS-CoV-2 main protease. J Biomol Struct Dyn 2023:1-12. [PMID: 37735887 DOI: 10.1080/07391102.2023.2257800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/02/2023] [Indexed: 09/23/2023]
Abstract
Since the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), several variants have caused a persistent pandemic. Consequently, it is crucial to develop new potential anti-SARS-CoV-2 drugs with specificity. To minimize potential failures and preserve valuable clinical resources for the development of other useful drugs, researchers must enhance their understanding of the interactions between drugs and SARS-CoV-2. While numerous crystal structures of the SARS-CoV-2 main protease (SCM) and its inhibitors have been reported, they provide only static snapshots and fail to capture the dynamic nature of SCM/inhibitor interactions. Herein, we conducted molecular dynamics simulations for five SCM complexes: ritonavir (SCM/RTV), lopinavir (SCM/LPV), the identified inhibitor N3 (SCM/N3), the approved inhibitor ensitrelvir (SCM/ESV), and the approved drug nirmatrelvir (SCM/NMV). Additionally, we explored the potential for covalent bond formation in the N3 and NMV inhibitors through QM/MM calculations using Umbrella sampling. The results show that the binding site is highly flexible to fit those five different inhibitors and each compound has its unique binding mode at the same binding site. Moreover, the binding affinities of positive and negative inhibitors to SCM exhibit significant differences. By gaining insights into the dynamics, we can potentially elucidate why lopinavir/ritonavir, initially considered promising, failed to effectively treat COVID-19. Furthermore, understanding the mechanistic aspects of N3 and NMV inhibition on SCM not only contributes to rational drug discovery against COVID-19 but also aids future studies on the catalytic mechanisms of main proteases in other novel coronaviruses.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hu Hongyu
- Xingzhi College, Zhejiang Normal University, Lanxi, China
| | - Tong Wu
- School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Fengming He
- School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Ma Chao
- MindRank AI Ltd, Hangzhou, China
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12
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Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
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Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
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13
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Vangala SR, Krishnan SR, Bung N, Srinivasan R, Roy A. pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules. J Chem Inf Model 2023; 63:5066-5076. [PMID: 37585609 DOI: 10.1021/acs.jcim.3c00689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.
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Affiliation(s)
- Sarveswara Rao Vangala
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | | | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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14
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Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel) 2023; 16:891. [PMID: 37375838 DOI: 10.3390/ph16060891] [Citation(s) in RCA: 47] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.
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Affiliation(s)
- Alexandre Blanco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Daniel Conde-Torres
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Rebeca Garcia-Fandino
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
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15
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Krishnan SR, Bung N, Padhi S, Bulusu G, Misra P, Pal M, Oruganti S, Srinivasan R, Roy A. De novo design of anti-tuberculosis agents using a structure-based deep learning method. J Mol Graph Model 2023; 118:108361. [PMID: 36257148 DOI: 10.1016/j.jmgm.2022.108361] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/10/2022] [Accepted: 10/07/2022] [Indexed: 11/28/2022]
Abstract
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Siladitya Padhi
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Gopalakrishnan Bulusu
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India; Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Parimal Misra
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Manojit Pal
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Srinivas Oruganti
- Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad, 500046, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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16
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An interpretable machine learning model for selectivity of small molecules against homologous protein family. Future Med Chem 2022; 14:1441-1453. [PMID: 36169035 DOI: 10.4155/fmc-2022-0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.
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17
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Chadi MA, Mousannif H, Aamouche A. Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator. J Cheminform 2022; 14:65. [PMID: 36167559 PMCID: PMC9516832 DOI: 10.1186/s13321-022-00643-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 09/07/2022] [Indexed: 11/10/2022] Open
Abstract
Deep learning has demonstrated promising results in de novo drug design. Often, the general pipeline consists of training a generative model (G) to learn the building rules of valid molecules, then using a biassing technique such as reinforcement learning (RL) to focus G on the desired chemical space. However, this sequential training of the same model for different tasks is known to be prone to a catastrophic forgetting (CF) phenomenon. This work presents a novel yet simple approach to bias G with significantly less CF than RL. The proposed method relies on backpropagating a reduced value of the cross-entropy loss used to train G according to the proportion of desired molecules that the biased-G can generate. We named our approach CRLV, short for conditional reduction of the loss value. We compared the two biased models (RL-biased-G and CRLV-biased-G) for four different objectives related to de novo drug design.CRLV-biased-G outperformed RL-biased-G in all four objectives and manifested appreciably less CF. Besides, an intersection analysis between molecules generated by the RL-biased-G and the CRLV-biased-G revealed that they can be used jointly without losing diversity given the low percentage of overlap between the two to further increase the desirability. Finally, we show that the difficulty of an objective is proportional to (i) its frequency in the dataset used to train G and (ii) the associated structural variance (SV), which is a new parameter we introduced in this paper, calling for novel exploration techniques for such difficult objectives.
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Affiliation(s)
- Mohamed-Amine Chadi
- Laboratoire Ingénierie des Systems Informatiques (LISI), Department of Computer Science, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakech, Morocco.
| | - Hajar Mousannif
- Laboratoire Ingénierie des Systems Informatiques (LISI), Department of Computer Science, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakech, Morocco
| | - Ahmed Aamouche
- Laboratoire Ingénierie des Systèmes et Applications (LISA), Ecole Nationale des Sciences Appliquées de Marrakech, Cadi Ayyad University, BP 575, Avenue Abdelkrim Khattabi, 40000, Marrakech, Morocco
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18
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Nepali K, Sharma R, Sharma S, Thakur A, Liou JP. Beyond the vaccines: a glance at the small molecule and peptide-based anti-COVID19 arsenal. J Biomed Sci 2022; 29:65. [PMID: 36064696 PMCID: PMC9444709 DOI: 10.1186/s12929-022-00847-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/16/2022] [Indexed: 02/08/2023] Open
Abstract
Unprecedented efforts of the researchers have been witnessed in the recent past towards the development of vaccine platforms for the control of the COVID-19 pandemic. Albeit, vaccination stands as a practical strategy to prevent SARS-CoV-2 infection, supplementing the anti-COVID19 arsenal with therapeutic options such as small molecules/peptides and antibodies is being conceived as a prudent strategy to tackle the emerging SARS-CoV-2 variants. Noteworthy to mention that collective efforts from numerous teams have led to the generation of a voluminous library composed of chemically and mechanistically diverse small molecules as anti-COVID19 scaffolds. This review article presents an overview of medicinal chemistry campaigns and drug repurposing programs that culminated in the identification of a plethora of small molecule-based anti-COVID19 drugs mediating their antiviral effects through inhibition of proteases, S protein, RdRp, ACE2, TMPRSS2, cathepsin and other targets. In light of the evidence ascertaining the potential of small molecule drugs to approach conserved proteins required for the viral replication of all coronaviruses, accelerated FDA approvals are anticipated for small molecules for the treatment of COVID19 shortly. Though the recent attempts invested in this direction in pursuit of enrichment of the anti-COVID-19 armoury (chemical tools) are praiseworthy, some strategies need to be implemented to extract conclusive benefits of the recently reported small molecule viz. (i) detailed preclinical investigation of the generated anti-COVID19 scaffolds (ii) in-vitro profiling of the inhibitors against the emerging SARS-CoV-2 variants (iii) development of assays enabling rapid screening of the libraries of anti-COVID19 scaffold (iv) leveraging the applications of machine learning based predictive models to expedite the anti-COVID19 drug discovery campaign (v) design of antibody-drug conjugates.
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Affiliation(s)
- Kunal Nepali
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
- TMU Research Center for Drug Discovery, Taipei Medical University, Taipei, 11031, Taiwan
| | - Ram Sharma
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Sachin Sharma
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Amandeep Thakur
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Jing-Ping Liou
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan.
- TMU Research Center for Drug Discovery, Taipei Medical University, Taipei, 11031, Taiwan.
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19
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Ruchawapol C, Fu WW, Xu HX. A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 104:154324. [PMID: 35841663 PMCID: PMC9259013 DOI: 10.1016/j.phymed.2022.154324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed. PURPOSE This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases. METHODS A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles. RESULTS In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks. CONCLUSION Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.
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Affiliation(s)
- Chattarin Ruchawapol
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China
| | - Wen-Wei Fu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China.
| | - Hong-Xi Xu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China; Engineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China.
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20
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Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
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Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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21
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Newly Emerged Antiviral Strategies for SARS-CoV-2: From Deciphering Viral Protein Structural Function to the Development of Vaccines, Antibodies, and Small Molecules. Int J Mol Sci 2022; 23:ijms23116083. [PMID: 35682761 PMCID: PMC9181103 DOI: 10.3390/ijms23116083] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/20/2022] [Accepted: 05/27/2022] [Indexed: 01/09/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become the most severe health crisis, causing extraordinary economic disruption worldwide. SARS-CoV-2 is a single-stranded RNA-enveloped virus. The process of viral replication and particle packaging is finished in host cells. Viral proteins, including both structural and nonstructural proteins, play important roles in the viral life cycle, which also provides the targets of treatment. Therefore, a better understanding of the structural function of virus proteins is crucial to speed up the development of vaccines and therapeutic strategies. Currently, the structure and function of proteins encoded by the SARS-CoV-2 genome are reviewed by several studies. However, most of them are based on the analysis of SARS-CoV-1 particles, lacking a systematic review update for SARS-CoV-2. Here, we specifically focus on the structure and function of proteins encoded by SARS-CoV-2. Viral proteins that contribute to COVID-19 infection and disease pathogenesis are reviewed according to the most recent research findings. The structure-function correlation of viral proteins provides a fundamental rationale for vaccine development and targeted therapy. Then, current antiviral vaccines are updated, such as inactive viral vaccines and protein-based vaccines and DNA, mRNA, and circular RNA vaccines. A summary of other therapeutic options is also reviewed, including monoclonal antibodies such as a cross-neutralizer antibody, a constructed cobinding antibody, a dual functional monoclonal antibody, an antibody cocktail, and an engineered bispecific antibody, as well as peptide-based inhibitors, chemical compounds, and clustered regularly interspaced short palindromic repeats (CRISPR) exploration. Overall, viral proteins and their functions provide the basis for targeted therapy and vaccine development.
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22
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Bung N, Krishnan SR, Roy A. An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System. J Chem Inf Model 2022; 62:2685-2695. [PMID: 35581002 DOI: 10.1021/acs.jcim.2c00462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.
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Affiliation(s)
- Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | | | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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23
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Albalawi U, Mustafa M. Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5901. [PMID: 35627437 PMCID: PMC9140632 DOI: 10.3390/ijerph19105901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/07/2022] [Accepted: 05/09/2022] [Indexed: 11/17/2022]
Abstract
SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.
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Affiliation(s)
- Umar Albalawi
- Faculty of Computing and Information Technology, University of Tabuk, KSA, Tabuk 71491, Saudi Arabia;
- Industrial Innovation and Robotics Center, University of Tabuk, KSA, Tabuk 71491, Saudi Arabia
| | - Mohammed Mustafa
- Faculty of Computing and Information Technology, University of Tabuk, KSA, Tabuk 71491, Saudi Arabia;
- Industrial Innovation and Robotics Center, University of Tabuk, KSA, Tabuk 71491, Saudi Arabia
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Floresta G, Zagni C, Gentile D, Patamia V, Rescifina A. Artificial Intelligence Technologies for COVID-19 De Novo Drug Design. Int J Mol Sci 2022; 23:ijms23063261. [PMID: 35328682 PMCID: PMC8949797 DOI: 10.3390/ijms23063261] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/16/2022] Open
Abstract
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.
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Yang F, Zhang Q, Ji X, Zhang Y, Li W, Peng S, Xue F. Machine Learning Applications in Drug Repurposing. Interdiscip Sci 2022; 14:15-21. [PMID: 35066811 PMCID: PMC8783773 DOI: 10.1007/s12539-021-00487-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/23/2021] [Accepted: 10/13/2021] [Indexed: 11/25/2022]
Abstract
The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.
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Affiliation(s)
- Fan Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Qi Zhang
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Xiaokang Ji
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Citie, Victoria University, Melbourne, Australia
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001 China
| | - Wentao Li
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
- School of Computer Science, National University of Defense Technology, Changsha, China
- Peng Cheng Lab, Shenzhen, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012 China
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26
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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Sridharan B, Goel M, Priyakumar UD. Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization. Chem Commun (Camb) 2022; 58:5316-5331. [DOI: 10.1039/d1cc07035e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied...
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Goel M, Raghunathan S, Laghuvarapu S, Priyakumar UD. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards. J Chem Inf Model 2021; 61:5815-5826. [PMID: 34866384 DOI: 10.1021/acs.jcim.1c01341] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
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Affiliation(s)
- Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.,École Centrale School of Engineering, Mahindra University, Hyderabad 500 043, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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29
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Welcome to Volume 14 of Future Medicinal Chemistry. Future Med Chem 2021; 14:1-3. [PMID: 34927463 DOI: 10.4155/fmc-2021-0300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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30
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Chávez-Hernández AL, Juárez-Mercado KE, Saldívar-González FI, Medina-Franco JL. Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products. Biomolecules 2021; 11:biom11121805. [PMID: 34944448 PMCID: PMC8698858 DOI: 10.3390/biom11121805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/22/2021] [Accepted: 11/29/2021] [Indexed: 01/14/2023] Open
Abstract
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.
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Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules. Front Genet 2021; 12:744170. [PMID: 34912370 PMCID: PMC8667578 DOI: 10.3389/fgene.2021.744170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/30/2021] [Indexed: 12/26/2022] Open
Abstract
Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.
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Affiliation(s)
- Emna Harigua-Souiai
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mahmoud Heinhane
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Yosser Zina Abdelkrim
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Oussama Souiai
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Ines Abdeljaoued-Tej
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Engineering School of Statistics and Information Analysis, University of Carthage, Ariana, Tunisia
| | - Ikram Guizani
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
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32
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Sousa T, Correia J, Pereira V, Rocha M. Generative Deep Learning for Targeted Compound Design. J Chem Inf Model 2021; 61:5343-5361. [PMID: 34699719 DOI: 10.1021/acs.jcim.0c01496] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.
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Affiliation(s)
- Tiago Sousa
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - João Correia
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
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33
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Krishnan SR, Bung N, Vangala SR, Srinivasan R, Bulusu G, Roy A. De Novo Structure-Based Drug Design Using Deep Learning. J Chem Inf Model 2021; 62:5100-5109. [PMID: 34792338 DOI: 10.1021/acs.jcim.1c01319] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In recent years, deep learning-based methods have emerged as promising tools for de novo drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties. Although there have been attempts to develop alternative ways to design target-specific ligand data sets, availability of such data sets remains a challenge while designing molecules against novel target proteins. In this work, we propose a deep learning-based method, where the knowledge of the active site structure of the target protein is sufficient to design new molecules. First, a graph attention model was used to learn the structure and features of the amino acids in the active site of proteins that are experimentally known to form protein-ligand complexes. Next, the learned active site features were used along with a pretrained generative model for conditional generation of new molecules. A bioactivity prediction model was then used in a reinforcement learning framework to optimize the conditional generative model. We validated our method against two well-studied proteins, Janus kinase 2 (JAK2) and dopamine receptor D2 (DRD2), where we produce molecules similar to the known inhibitors. The graph attention model could identify the probable key active site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Sarveswara Rao Vangala
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Gopalakrishnan Bulusu
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India
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Arshia AH, Shadravan S, Solhjoo A, Sakhteman A, Sami A. De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations. Comput Biol Med 2021; 139:104967. [PMID: 34739968 PMCID: PMC8545757 DOI: 10.1016/j.compbiomed.2021.104967] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 12/15/2022]
Abstract
The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates capable of inhibiting the main proteases in SARS-CoV-2. The network was fine-tuned over ten generations, with each generation resulting in higher binding affinity scores. The binding affinities and interactions between the selected candidates and the SARS-CoV-2 main protease are predicted using a molecular docking simulation using AutoDock Vina. The compounds selected have a strong interaction with the key MET 165 and Cys145 residues. Molecular dynamics (MD) simulations were run for 150ns to validate the docking results on the top four ligands. Additionally, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and hydrogen bond analysis strongly support these findings. Furthermore, the MM-PBSA free energy calculations revealed that these chemical molecules have stable and favorable energies, resulting in a strong binding with Mpro's binding site. This study's extensive computational and statistical analyses indicate that the selected candidates may be used as potential inhibitors against the SARS-CoV-2 in-silico environment. However, additional in-vitro, in-vivo, and clinical trials are required to demonstrate their true efficacy.
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Affiliation(s)
- Amir Hossein Arshia
- CSE and IT Department; School of Electrical Engineering and Computer; Shiraz University, Shiraz, Iran
| | - Shayan Shadravan
- CSE and IT Department; School of Electrical Engineering and Computer; Shiraz University, Shiraz, Iran
| | - Aida Solhjoo
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amirhossein Sakhteman
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Ashkan Sami
- CSE and IT Department; School of Electrical Engineering and Computer; Shiraz University, Shiraz, Iran.
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Lv H, Shi L, Berkenpas JW, Dao FY, Zulfiqar H, Ding H, Zhang Y, Yang L, Cao R. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform 2021; 22:bbab320. [PMID: 34410360 PMCID: PMC8511807 DOI: 10.1093/bib/bbab320] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.
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Affiliation(s)
- Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai 200433, China
| | | | - Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Liming Yang
- Department of Pathophysiology, Harbin Medical University-Daqing, Daqing, 163319, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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Liu Q, Wan J, Wang G. A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Brief Bioinform 2021; 23:6384382. [PMID: 34623382 PMCID: PMC8524468 DOI: 10.1093/bib/bbab416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [
1–
5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.
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Affiliation(s)
- Qiaoming Liu
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Guohua Wang
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China.,Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150001, China
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Hu F, Huang M, Sun J, Zhang X, Liu J. An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2021; 73:11-21. [PMID: 33679271 PMCID: PMC7919532 DOI: 10.1016/j.inffus.2021.02.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/18/2020] [Accepted: 02/21/2021] [Indexed: 05/04/2023]
Abstract
Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.
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Affiliation(s)
- Fang Hu
- College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, PR China
- Department of Mathematics and Statistics, University of West Florida, Pensacola 32514, USA
| | - Mingfang Huang
- College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, PR China
| | - Jing Sun
- Department of Data Center, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
| | - Xiong Zhang
- Department of Geriatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
| | - Jifen Liu
- Department of Data Center, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430060, PR China
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Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100042. [PMID: 34870150 PMCID: PMC8317454 DOI: 10.1016/j.crphar.2021.100042] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.
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Affiliation(s)
- Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS-CoV-2. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6696012. [PMID: 34124259 PMCID: PMC8172298 DOI: 10.1155/2021/6696012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 05/07/2021] [Accepted: 05/15/2021] [Indexed: 12/04/2022]
Abstract
A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world economy. Indeed, following the emergence of this new virus, despite the existence of a few approved and known effective vaccines at the time of writing this original study, a sense of urgency has emerged worldwide to discover new technical tools and new drugs as soon as possible. In this context, many studies and researches are currently underway to develop new tools and therapies against SARS CoV-2 and other viruses, using different approaches. The 3-chymotrypsin (3CL) protease, which is directly involved in the cotranslational and posttranslational modifications of viral polyproteins essential for the existence and replication of the virus in the host, is one of the coronavirus target proteins that has been the subject of these extensive studies. Currently, the majority of these studies are aimed at repurposing already known and clinically approved drugs against this new virus, but this approach is not really successful. Recently, different studies have successfully demonstrated the effectiveness of artificial intelligence-based techniques to understand existing chemical spaces and generate new small molecules that are both effective and efficient. In this framework and for our study, we combined a generative recurrent neural network model with transfer learning methods and active learning-based algorithms to design novel small molecules capable of effectively inhibiting the 3CL protease in human cells. We then analyze these small molecules to find the correct binding site that matches the structure of the 3CL protease of our target virus as well as other analyses performed in this study. Based on these screening results, some molecules have achieved a good binding score close to -18 kcal/mol, which we can consider as good potential candidates for further synthesis and testing against SARS-CoV-2.
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Srinivasan S, Batra R, Chan H, Kamath G, Cherukara MJ, Sankaranarayanan SKRS. Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19. ACS OMEGA 2021; 6:12557-12566. [PMID: 34056406 PMCID: PMC8154149 DOI: 10.1021/acsomega.1c00477] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/18/2021] [Indexed: 05/14/2023]
Abstract
An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.
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Affiliation(s)
- Srilok Srinivasan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Rohit Batra
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Henry Chan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Department
of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
| | - Ganesh Kamath
- Dalzielfiver
LLC, 3500 Carlfield Street, El Sobrante, California 94803, United States
| | - Mathew J. Cherukara
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Advanced
Photon Source, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Subramanian K. R. S. Sankaranarayanan
- Center
for Nanoscale Materials, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Department
of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- , . Phone: +1 (312) 355-6770
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Bajad NG, Rayala S, Gutti G, Sharma A, Singh M, Kumar A, Singh SK. Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100026. [PMID: 34870145 PMCID: PMC8120892 DOI: 10.1016/j.crphar.2021.100026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 12/26/2022] Open
Abstract
The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.
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Affiliation(s)
- Nilesh Gajanan Bajad
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Swetha Rayala
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Gopichand Gutti
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Anjali Sharma
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Meenakshi Singh
- Department of Medicinal Chemistry, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Ashok Kumar
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Sushil Kumar Singh
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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Aksoy B, Salman OKM. Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1908431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Bekir Aksoy
- Department of Mechatronics Engineering, Isparta University of Applied Sciences, Isparta, Turkey
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Serafim MS, Gertrudes JC, Costa DM, Oliveira PR, Maltarollo VG, Honorio KM. Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates. Biosci Rep 2021; 41:BSR20202616. [PMID: 33624754 PMCID: PMC7982772 DOI: 10.1042/bsr20202616] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/15/2021] [Accepted: 02/23/2021] [Indexed: 01/18/2023] Open
Abstract
Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.
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Affiliation(s)
- Mateus S.M. Serafim
- Department of Microbiology, Biological Sciences Institute, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Jadson C. Gertrudes
- Department of Computer Science, Federal University of Ouro Preto (UFOP), Ouro Preto, MG, Brazil
| | - Débora M.A. Costa
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Patricia R. Oliveira
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
| | - Vinicius G. Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Kathia M. Honorio
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
- Center for Natural and Human Sciences, Federal University of ABC (UFABC), Santo Andre, SP, Brazil
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Born J, Manica M, Cadow J, Markert G, Mill NA, Filipavicius M, Janakarajan N, Cardinale A, Laino T, Rodríguez Martínez M. Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abe808] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Abstract
Bridging systems biology and drug design, we propose a deep learning framework for de novo discovery of molecules tailored to bind with given protein targets. Our methodology is exemplified by the task of designing antiviral candidates to target SARS-CoV-2 related proteins. Crucially, our framework does not require fine-tuning for specific proteins but is demonstrated to generalize in proposing ligands with high predicted binding affinities against unseen targets. Coupling our framework with the automatic retrosynthesis prediction of IBM RXN for Chemistry, we demonstrate the feasibility of swift chemical synthesis of molecules with potential antiviral properties that were designed against a specific protein target. In particular, we synthesize an antiviral candidate designed against the host protein angiotensin converting enzyme 2 (ACE2); a surface receptor on human respiratory epithelial cells that facilitates SARS-CoV-2 cell entry through its spike glycoprotein.
This is achieved as follows. First, we train a multimodal ligand–protein binding affinity model on predicting affinities of bioactive compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator that consists of two variational autoencoders (VAE), our framework steers the generation toward regions of the chemical space with high-reward molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep reinforcement learning, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling binding ligands, with an average increase of 83% comparing to an unbiased VAE. The generated molecules exhibit favorable properties in terms of target binding affinity, selectivity and drug-likeness. We use molecular retrosynthetic models to provide a synthetic accessibility assessment of the best generated hit molecules. Finally, with this end-to-end framework, we synthesize 3-Bromobenzylamine, a potential inhibitor of the host ACE2 protein, solely based on the recommendations of a molecular retrosynthesis model and a synthesis protocol prediction model. We hope that our framework can contribute towards swift discovery of de novo molecules with desired pharmacological properties.
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Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031117. [PMID: 33513984 PMCID: PMC7908539 DOI: 10.3390/ijerph18031117] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/16/2021] [Accepted: 01/17/2021] [Indexed: 12/13/2022]
Abstract
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
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Affiliation(s)
- Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
- Correspondence:
| | - Abdul Muneeim Tehame
- Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan;
| | - Saleh Bajaba
- Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Ahmed Barnawi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Saad Zia
- IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia;
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