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Kou X, Shi P, Gao C, Ma P, Xing H, Ke Q, Zhang D. Data-Driven Elucidation of Flavor Chemistry. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:6789-6802. [PMID: 37102791 PMCID: PMC10176570 DOI: 10.1021/acs.jafc.3c00909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.
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Affiliation(s)
- Xingran Kou
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Peiqin Shi
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Chukun Gao
- Laboratory for Physical Chemistry, ETH Zürich, 8093 Zürich, Switzerland
| | - Peihua Ma
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinfei Ke
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Dachuan Zhang
- National Centre of Competence in Research (NCCR) Catalysis, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
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52
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Wang Z, Gao H. Anti-inflammatory or anti-SARS-CoV-2 ingredients in Huashi Baidu Decoction and their corresponding targets: Target screening and molecular docking study. ARAB J CHEM 2023; 16:104663. [PMID: 36816510 PMCID: PMC9928610 DOI: 10.1016/j.arabjc.2023.104663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a rapidly emerging infectious disease caused by SARS-CoV-2. Inflammatory factors may play essential roles in COVID-19 progression. Huashi Baidu Decoction (HSBD) is a traditional Chinese medicine (TCM) formula that can expel cold, dispel dampness, and reduce inflammation. HSBD has been widely used for the treatment of COVID-19. However, the active ingredients and potential targets for HSBD to exert anti-inflammatory or anti-SARS-CoV-2 effects remain unclear. In this paper, the active ingredients with anti-inflammatory or anti-viral effects in HSBD and their potential targets were screened using the Discovery Studio 2020 software. By overlapping the targets of HSBD and COVID-19, 8 common targets (FYN, SFTPD, P53, RBP4, IL1RN, TTR, SRPK1, and AKT1) were identified. We determined 2 key targets (P53 and AKT1) by network pharmacology. The main active ingredients in HSBD were evaluated using the key targets as receptor proteins for molecular docking. The results suggested that the best active ingredients Kaempferol2 and Kaempferol3 have the potential as supplements for the treatment of COVID-19.
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Affiliation(s)
| | - Hongwei Gao
- Corresponding author at: Prof Hongwei Gao: School of Life Science, Ludong University, Yantai, Shandong, 264025, China
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53
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Yu Y, Cai C, Wang J, Bo Z, Zhu Z, Zheng H. Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening. J Chem Theory Comput 2023. [PMID: 37125970 DOI: 10.1021/acs.jctc.2c01145] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.
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Affiliation(s)
- Yuejiang Yu
- Beijing DP Technology Co., Ltd., Beijing 100080, China
- School of EECS, Peking University, Beijing 100871, China
| | - Chun Cai
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Jiayue Wang
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Zonghua Bo
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Zhengdan Zhu
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Hang Zheng
- Beijing DP Technology Co., Ltd., Beijing 100080, China
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54
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Carneiro J, Magalhães RP, de la Oliva Roque VM, Simões M, Pratas D, Sousa SF. TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa. J Comput Aided Mol Des 2023; 37:265-278. [PMID: 37085636 DOI: 10.1007/s10822-023-00505-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/12/2023] [Indexed: 04/23/2023]
Abstract
Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.
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Affiliation(s)
- João Carneiro
- Interdisciplinary Centre of Marine and Environmental Research, CIIMAR, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, Porto, 4450-208, Portugal.
| | - Rita P Magalhães
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Victor M de la Oliva Roque
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Manuel Simões
- Faculty of Engineering, LEPABE Laboratory for Process Engineering, Environment, Biotechnology and Energy, University of Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal
- Faculty of Engineering, ALiCE-Associate Laboratory in Chemical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - Diogo Pratas
- Institute of Electronics and Informatics Engineering of Aveiro, IEETA, University of Aveiro, Aveiro, Portugal
- Department of Electronics, Telecommunications and Informatics, DETI, University of Aveiro, Aveiro, Portugal
- Department of Virology, DoV, University of Helsinki, Helsinki, Finland
| | - Sérgio F Sousa
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
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55
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Cavasotto CN, Di Filippo JI. The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking. J Chem Inf Model 2023; 63:2267-2280. [PMID: 37036491 DOI: 10.1021/acs.jcim.2c01471] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.
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Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
| | - Juan I Di Filippo
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
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56
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Ang D, Kendall R, Atamian HS. Virtual and In Vitro Screening of Natural Products Identifies Indole and Benzene Derivatives as Inhibitors of SARS-CoV-2 Main Protease (M pro). BIOLOGY 2023; 12:biology12040519. [PMID: 37106720 PMCID: PMC10135783 DOI: 10.3390/biology12040519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 04/29/2023]
Abstract
The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with the current computation advancements. Here, a dataset of 406,747 unique NPs was screened against the SARS-CoV-2 main protease (Mpro) crystal structure (6lu7) using a combination of ligand- and structural-based virtual screening. Based on 1) the predicted binding affinities of the NPs to the Mpro, 2) the types and number of interactions with the Mpro amino acids that are critical for its function, and 3) the desirable pharmacokinetic properties of the NPs, we identified the top 20 candidates that could potentially inhibit the Mpro protease function. A total of 7 of the 20 top candidates were subjected to in vitro protease inhibition assay and 4 of them (4/7; 57%), including two beta carbolines, one N-alkyl indole, and one Benzoic acid ester, had significant inhibitory activity against Mpro protease. These four NPs could be developed further for the treatment of COVID-19 symptoms.
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Affiliation(s)
- Dony Ang
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Riley Kendall
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Hagop S Atamian
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
- Biological Sciences Program, Chapman University, Orange, CA 92866, USA
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57
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Grasso D, Galderisi S, Santucci A, Bernini A. Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology. Int J Mol Sci 2023; 24:ijms24065819. [PMID: 36982893 PMCID: PMC10054308 DOI: 10.3390/ijms24065819] [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: 02/05/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
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Affiliation(s)
- Daniela Grasso
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Silvia Galderisi
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Andrea Bernini
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
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58
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Bian Y, Kwon JJ, Liu C, Margiotta E, Shekhar M, Gould AE. Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification. Front Mol Biosci 2023; 10:1163536. [PMID: 36994428 PMCID: PMC10040869 DOI: 10.3389/fmolb.2023.1163536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 02/28/2023] [Indexed: 03/15/2023] Open
Abstract
High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.
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Affiliation(s)
- Yuemin Bian
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- *Correspondence: Yuemin Bian,
| | - Jason J. Kwon
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Enrico Margiotta
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Mrinal Shekhar
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Alexandra E. Gould
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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59
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Quancard J, Vulpetti A, Bach A, Cox B, Guéret SM, Hartung IV, Koolman HF, Laufer S, Messinger J, Sbardella G, Craft R. The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit Generation. ChemMedChem 2023; 18:e202300002. [PMID: 36892096 DOI: 10.1002/cmdc.202300002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/14/2023] [Indexed: 03/10/2023]
Abstract
Hit generation is a crucial step in drug discovery that will determine the speed and chance of success of identifying drug candidates. Many strategies are now available to identify chemical starting points, or hits, and each biological target warrants a tailored approach. In this set of best practices, we detail the essential approaches for target centric hit generation and the opportunities and challenges they come with. We then provide guidance on how to validate hits to ensure medicinal chemistry is only performed on compounds and scaffolds that engage the target of interest and have the desired mode of action. Finally, we discuss the design of integrated hit generation strategies that combine several approaches to maximize the chance of identifying high quality starting points to ensure a successful drug discovery campaign.
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Affiliation(s)
- Jean Quancard
- Global Discovery Chemistry, Novartis Institute for Biomedical Research, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland
| | - Anna Vulpetti
- Global Discovery Chemistry, Novartis Institute for Biomedical Research, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland
| | - Anders Bach
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - Brian Cox
- School of Life Sciences, University of Sussex, Brighton, BN1 9RH, UK
| | - Stéphanie M Guéret
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden
| | - Ingo V Hartung
- Medicinal Chemistry, Global R&D, Merck Healthcare KGaA, Frankfurter Straße 250, 64293, Darmstadt, Germany
| | - Hannes F Koolman
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany
| | - Stefan Laufer
- Pharmaceutical & Medicinal Chemistry, Institute of Pharmacy & Biochemistry, Tübingen Center for Academic Drug Discovery, Auf der Morgenstelle 8, 72070, Tübingen, Germany
| | - Josef Messinger
- Medicine Design, Orionpharma, Orionintie 1, 02101, Espoo, Finland
| | - Gianluca Sbardella
- Department of Pharmacy, Epigenetic Med Chem Lab, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano (SA), Italy
| | - Russell Craft
- Medicinal chemistry, Symeres, Kadijk 3, 9747 AT, Groningen, The Netherlands
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60
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Tonolo F, Grinzato A, Bindoli A, Rigobello MP. From In Silico to a Cellular Model: Molecular Docking Approach to Evaluate Antioxidant Bioactive Peptides. Antioxidants (Basel) 2023; 12:antiox12030665. [PMID: 36978913 PMCID: PMC10045749 DOI: 10.3390/antiox12030665] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/10/2023] Open
Abstract
The increasing need to counteract the redox imbalance in chronic diseases leads to focusing research on compounds with antioxidant activity. Among natural molecules with health-promoting effects on many body functions, bioactive peptides are gaining interest. They are protein fragments of 2–20 amino acids that can be released by various mechanisms, such as gastrointestinal digestion, food processing and microbial fermentation. Recent studies report the effects of bioactive peptides in the cellular environment, and there is evidence that these compounds can exert their action by modulating specific pathways. This review focuses on the newest approaches to the structure–function correlation of the antioxidant bioactive peptides, considering their molecular mechanism, by evaluating the activation of specific signaling pathways that are linked to antioxidant systems. The correlation between the results of in silico molecular docking analysis and the effects in a cellular model was highlighted. This knowledge is fundamental in order to propose the use of bioactive peptides as ingredients in functional foods or nutraceuticals.
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Affiliation(s)
- Federica Tonolo
- Department of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell’Università, 35020 Padova, Italy
| | - Alessandro Grinzato
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38000 Grenoble, France
| | - Alberto Bindoli
- Institute of Neuroscience (CNR), Viale G. Colombo 3, 35131 Padova, Italy
| | - Maria Pia Rigobello
- Department of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy
- Correspondence:
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Clyde A, Liu X, Brettin T, Yoo H, Partin A, Babuji Y, Blaiszik B, Mohd-Yusof J, Merzky A, Turilli M, Jha S, Ramanathan A, Stevens R. AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection. Sci Rep 2023; 13:2105. [PMID: 36747041 PMCID: PMC9901402 DOI: 10.1038/s41598-023-28785-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
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Affiliation(s)
- Austin Clyde
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, 60637, USA.
| | - Xuefeng Liu
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Thomas Brettin
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
| | - Hyunseung Yoo
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Alexander Partin
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Yadu Babuji
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Ben Blaiszik
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
- University of Chicago, Globus, Chicago, 60637, USA
| | - Jamaludin Mohd-Yusof
- Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences, Los Alamos, 87545, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Shantenu Jha
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Arvind Ramanathan
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Rick Stevens
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
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New avenues in artificial-intelligence-assisted drug discovery. Drug Discov Today 2023; 28:103516. [PMID: 36736583 DOI: 10.1016/j.drudis.2023.103516] [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: 08/07/2022] [Revised: 12/08/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023]
Abstract
Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation technology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mining such data and extracting useful patterns. Because the identification of chemical entities with desired biological activity is a crucial task in drug discovery, AI technologies have the potential to accelerate this process and support decision making. In addition, the advent of deep learning (DL) has shown great promise in addressing diverse problems in drug discovery, such as de novo molecular design. Herein, we will appraise the current state-of-the-art in AI-assisted drug discovery, discussing the recent applications covering generative models for chemical structure generation, scoring functions to improve binding affinity and pose prediction, and molecular dynamics to assist in the parametrization, featurization and generalization tasks. Finally, we will discuss current hurdles and the strategies to overcome them, as well as potential future directions.
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63
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Iype E, Pillai U J, Kumar I, Gaastra-Nedea SV, Subramanian R, Saha RN, Dutta M. In silico and in vitro assays reveal potential inhibitors against 3CL pro main protease of SARS-CoV-2. J Biomol Struct Dyn 2022; 40:12800-12811. [PMID: 34550861 DOI: 10.1080/07391102.2021.1977181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is not showing any sign of slowing down even after the ongoing efforts of vaccination. The threats of new strains are concerning, as some of them are more infectious than the original one. A therapeutic against the disease is, therefore, of urgent need. Here, we use the DrugBank database to screen for potential inhibitors against the 3CLpro main protease of SARS-CoV-2. Instead of using the traditional approach of computational screening by docking, we developed a kernel ridge regressor (using a part of the docking data) to predict the binding energy of ligands. We used this model to screen the DrugBank database and shortlist two lead candidates (bromocriptine and avoralstat) for in vitro enzymatic study. Our results show that the 3CLpro enzyme activity in presence of 100 μM concentration of bromocriptine and avoralstat is 9.9% and 15.9%, respectively. Remarkably, bromocriptine exhibited submicromolar IC50 of 130 nM (0.13 μM). Avoralstat showed an IC50 of 2.16 μM. Further, the interactions of both drugs with 3CLpro were analyzed using molecular dynamics simulations of 100 ns. Results indicate that both ligands are stable in the binding pocket of the 3CLpro receptor. In addition, the MM-PBSA analysis revealed that bromocriptine (-29.37 kcal/mol) has a lower binding free energy compared to avoralstat (-6.91 kcal/mol). Further, hydrogen bond analysis also showed that bromocriptine interacts with the two catalytic residues, His41 and Cys145, more frequently than avoralstat.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Eldhose Iype
- Department of Chemical Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
| | - Jisha Pillai U
- Department of Biotechnology, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
| | - Indresh Kumar
- Department of Chemistry, BITS Pilani, Pilani Campus, Pilani, India
| | - Silvia V Gaastra-Nedea
- Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | | | - Mainak Dutta
- Department of Biotechnology, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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van Tilborg D, Alenicheva A, Grisoni F. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs. J Chem Inf Model 2022; 62:5938-5951. [PMID: 36456532 PMCID: PMC9749029 DOI: 10.1021/acs.jcim.2c01073] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Indexed: 12/03/2022]
Abstract
Machine learning has become a crucial tool in drug discovery and chemistry at large, e.g., to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure but exhibit large differences in potency─have received limited attention for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization but also models that are well equipped to accurately predict the potency of activity cliffs have increased potential for prospective applications. Our work aims to fill the current knowledge gap on best-practice machine learning methods in the presence of activity cliffs. We benchmarked a total of 24 machine and deep learning approaches on curated bioactivity data from 30 macromolecular targets for their performance on activity cliff compounds. While all methods struggled in the presence of activity cliffs, machine learning approaches based on molecular descriptors outperformed more complex deep learning methods. Our findings highlight large case-by-case differences in performance, advocating for (a) the inclusion of dedicated "activity-cliff-centered" metrics during model development and evaluation and (b) the development of novel algorithms to better predict the properties of activity cliffs. To this end, the methods, metrics, and results of this study have been encapsulated into an open-access benchmarking platform named MoleculeACE (Activity Cliff Estimation, available on GitHub at: https://github.com/molML/MoleculeACE). MoleculeACE is designed to steer the community toward addressing the pressing but overlooked limitation of molecular machine learning models posed by activity cliffs.
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Affiliation(s)
- Derek van Tilborg
- Institute
for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612AZEindhoven, The Netherlands
- Centre
for Living Technologies, Alliance TU/e,
WUR, UU, UMC Utrecht, 3584CBUtrecht, The Netherlands
| | | | - Francesca Grisoni
- Institute
for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612AZEindhoven, The Netherlands
- Centre
for Living Technologies, Alliance TU/e,
WUR, UU, UMC Utrecht, 3584CBUtrecht, The Netherlands
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66
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Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PL pro Inhibitors. Molecules 2022; 27:molecules27238569. [PMID: 36500659 PMCID: PMC9741453 DOI: 10.3390/molecules27238569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.
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67
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Bajorath J, Chávez-Hernández AL, Duran-Frigola M, Fernández-de Gortari E, Gasteiger J, López-López E, Maggiora GM, Medina-Franco JL, Méndez-Lucio O, Mestres J, Miranda-Quintana RA, Oprea TI, Plisson F, Prieto-Martínez FD, Rodríguez-Pérez R, Rondón-Villarreal P, Saldívar-Gonzalez FI, Sánchez-Cruz N, Valli M. Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J Cheminform 2022; 14:82. [PMID: 36461094 PMCID: PMC9716667 DOI: 10.1186/s13321-022-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .
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Affiliation(s)
- Jürgen Bajorath
- grid.10388.320000 0001 2240 3300Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53113 Bonn, Germany
| | - Ana L. Chávez-Hernández
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Miquel Duran-Frigola
- Ersilia Open Source Initiative, Cambridge, UK ,grid.7722.00000 0001 1811 6966Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia Spain
| | - Eli Fernández-de Gortari
- grid.420330.60000 0004 0521 6935Nanosafety Laboratory, International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal
| | - Johann Gasteiger
- grid.5330.50000 0001 2107 3311Computer-Chemie-Centrum, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Edgar López-López
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico ,grid.512574.0Department of Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), 07360 Mexico City, Mexico
| | - Gerald M. Maggiora
- grid.134563.60000 0001 2168 186XBIO5 Institute, University of Arizona, Tucson, AZ 85721 USA
| | - José L. Medina-Franco
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | | | - Jordi Mestres
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.20522.370000 0004 1767 9005Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomedica (PRBB), 08003 Barcelona, Catalonia Spain
| | | | - Tudor I. Oprea
- grid.266832.b0000 0001 2188 8502Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA ,grid.8761.80000 0000 9919 9582Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at Gothenburg University, 40530 Gothenburg, Sweden ,grid.5254.60000 0001 0674 042XNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark ,Present Address: Roivant Discovery Sciences, Inc., 451 D Street, Boston, MA 02210 USA
| | - Fabien Plisson
- grid.512574.0Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824 Irapuato, Gto Mexico
| | - Fernando D. Prieto-Martínez
- grid.9486.30000 0001 2159 0001Chemistry Institute, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Raquel Rodríguez-Pérez
- grid.419481.10000 0001 1515 9979Novartis Institutes for Biomedical Research, 4002 Basel, Switzerland
| | - Paola Rondón-Villarreal
- grid.442204.40000 0004 0486 1035Universidad de Santander, Facultad de Ciencias Médicas y de la Salud, Instituto de Investigación Masira, Calle 70 No. 55-210, 680003 Santander, Bucaramanga Colombia
| | - Fernanda I. Saldívar-Gonzalez
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Norberto Sánchez-Cruz
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.9486.30000 0001 2159 0001Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz Km. 4.5, Yucatán, 97357 Ucú, Mexico
| | - Marilia Valli
- grid.410543.70000 0001 2188 478XNuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University-UNESP, Araraquara, Brazil
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68
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Kumar N, Acharya V. Machine intelligence-driven framework for optimized hit selection in virtual screening. J Cheminform 2022; 14:48. [PMID: 35869511 PMCID: PMC9306080 DOI: 10.1186/s13321-022-00630-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractVirtual screening (VS) aids in prioritizing unknown bio-interactions between compounds and protein targets for empirical drug discovery. In standard VS exercise, roughly 10% of top-ranked molecules exhibit activity when examined in biochemical assays, which accounts for many false positive hits, making it an arduous task. Attempts for conquering false-hit rates were developed through either ligand-based or structure-based VS separately; however, nonetheless performed remarkably well. Here, we present an advanced VS framework—automated hit identification and optimization tool (A-HIOT)—comprises chemical space-driven stacked ensemble for identification and protein space-driven deep learning architectures for optimization of an array of specific hits for fixed protein receptors. A-HIOT implements numerous open-source algorithms intending to integrate chemical and protein space leading to a high-quality prediction. The optimized hits are the selective molecules which we retrieve after extreme refinement implying chemical space and protein space modules of A-HIOT. Using CXC chemokine receptor 4, we demonstrated the superior performance of A-HIOT for hit molecule identification and optimization with tenfold cross-validation accuracies of 94.8% and 81.9%, respectively. In comparison with other machine learning algorithms, A-HIOT achieved higher accuracies of 96.2% for hit identification and 89.9% for hit optimization on independent benchmark datasets for CXCR4 and 86.8% for hit identification and 90.2% for hit optimization on independent test dataset for androgen receptor (AR), thus, shows its generalizability and robustness. In conclusion, advantageous features impeded in A-HIOT is making a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS in finding the optimized hits for the desired receptor. The complete resource (framework) code is available at https://gitlab.com/neeraj-24/A-HIOT.
Graphical Abstract
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69
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Discovery of Novel Lin28 Inhibitors to Suppress Cancer Cell Stemness. Cancers (Basel) 2022; 14:cancers14225687. [PMID: 36428779 PMCID: PMC9688808 DOI: 10.3390/cancers14225687] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022] Open
Abstract
Lin28 is a pluripotency factor that regulates cancer cell stem-like phenotypes to promote cancer development and therapy-resistant tumor progression. It acts through its cold shock domain and zinc knuckle domain (ZKD) to interact with the Let-7 pre-microRNA and block Let-7 biosynthesis. Chemical inhibition of Lin28 from interacting with Let-7 presents a therapeutic strategy for cancer therapy. Herein, we present the computer-aided development of small molecules by in silico screening 18 million compounds from the ZINC20 library, followed by the biological validation of 163 predicted compounds to confirm 15 new Lin28 inhibitors. We report three lead compounds, Ln7, Ln15, and Ln115, that target the ZKD of both Lin28A and Lin28B isoforms and block Lin28 from binding Let-7. They restore Let-7 expression and suppress tumor oncogenes such as SOX2 in cancer cells and show strong inhibitory effects on cancer cell stem-like phenotypes. However, minimal impacts of these compounds were observed on Lin28-negative cells, confirming the on-target effects of these compounds. We conclude from this study the discovery of several new Lin28 inhibitors as promising candidate compounds that warrant further drug development into potential anticancer therapies.
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70
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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71
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Blanchard AE, Gounley J, Bhowmik D, Chandra Shekar M, Lyngaas I, Gao S, Yin J, Tsaris A, Wang F, Glaser J. Language models for the prediction of SARS-CoV-2 inhibitors. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2022; 36:587-602. [PMID: 38603308 PMCID: PMC9548488 DOI: 10.1177/10943420221121804] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.
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Affiliation(s)
| | - John Gounley
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | | | - Shang Gao
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Junqi Yin
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | - Feiyi Wang
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jens Glaser
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
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72
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Choi SYC, Ribeiro CF, Wang Y, Loda M, Plymate SR, Uo T. Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer. Biomolecules 2022; 12:1590. [PMID: 36358940 PMCID: PMC9687810 DOI: 10.3390/biom12111590] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 08/27/2023] Open
Abstract
There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.
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Affiliation(s)
- Stephen Y. C. Choi
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
- Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Caroline Fidalgo Ribeiro
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, NY 10021, USA
| | - Yuzhuo Wang
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
- Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, NY 10021, USA
- New York Genome Center, New York, NY 10013, USA
| | - Stephen R. Plymate
- Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
- Geriatrics Research Education and Clinical Center, VA Puget Sound Health Care System, Seattle, WA 98108, USA
| | - Takuma Uo
- Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
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73
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Wong F, Krishnan A, Zheng EJ, Stärk H, Manson AL, Earl AM, Jaakkola T, Collins JJ. Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Mol Syst Biol 2022; 18:e11081. [PMID: 36065847 PMCID: PMC9446081 DOI: 10.15252/msb.202211081] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/12/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022] Open
Abstract
Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.
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Affiliation(s)
- Felix Wong
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aarti Krishnan
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erica J Zheng
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Program in Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Hannes Stärk
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Abigail L Manson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ashlee M Earl
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tommi Jaakkola
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James J Collins
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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74
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Pandey M, Radaeva M, Mslati H, Garland O, Fernandez M, Ester M, Cherkasov A. Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks. Molecules 2022; 27:molecules27165114. [PMID: 36014351 PMCID: PMC9416537 DOI: 10.3390/molecules27165114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.
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Affiliation(s)
- Mohit Pandey
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Mariia Radaeva
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Hazem Mslati
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Olivia Garland
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
- Correspondence:
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75
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García-Ortegón M, Simm GNC, Tripp AJ, Hernández-Lobato JM, Bender A, Bacallado S. DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J Chem Inf Model 2022; 62:3486-3502. [PMID: 35849793 PMCID: PMC9364321 DOI: 10.1021/acs.jcim.1c01334] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Indexed: 01/05/2023]
Abstract
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.
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Affiliation(s)
- Miguel García-Ortegón
- Statistical
Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd., Cambridge CB3 0WB, United Kingdom
| | - Gregor N. C. Simm
- Department
of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, United Kingdom
| | - Austin J. Tripp
- Department
of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, United Kingdom
| | | | - Andreas Bender
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Rd., Cambridge CB2 1EW, United Kingdom
| | - Sergio Bacallado
- Statistical
Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd., Cambridge CB3 0WB, United Kingdom
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76
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Graff DE, Aldeghi M, Morrone JA, Jordan KE, Pyzer-Knapp EO, Coley CW. Self-Focusing Virtual Screening with Active Design Space Pruning. J Chem Inf Model 2022; 62:3854-3862. [DOI: 10.1021/acs.jcim.2c00554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- David E. Graff
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Matteo Aldeghi
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Joseph A. Morrone
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10594, United States
| | - Kirk E. Jordan
- IBM Thomas J. Watson Research Center, Cambridge, Massachusetts 02142, United States
| | | | - Connor W. Coley
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02142, United States
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77
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Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2. Struct Chem 2022; 33:1585-1608. [PMID: 35938064 PMCID: PMC9346052 DOI: 10.1007/s11224-022-02020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/19/2022] [Indexed: 11/21/2022]
Abstract
The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.
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78
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Gorgulla C, Jayaraj A, Fackeldey K, Arthanari H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr Opin Chem Biol 2022; 69:102156. [PMID: 35576813 PMCID: PMC9990419 DOI: 10.1016/j.cbpa.2022.102156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/16/2022] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
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Affiliation(s)
- Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany; Zuse Institute Berlin, Berlin, Germany
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.
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79
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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80
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Geoffrey B, Sanker A, Madaj R, Tresanco MSV, Upadhyay M, Gracia J. A program to automate the discovery of drugs for West Nile and Dengue virus—programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated in silico modelling. J Biomol Struct Dyn 2022; 40:4293-4300. [DOI: 10.1080/07391102.2020.1856185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Ben Geoffrey
- University of Madras, Chepauk, Chennai, Tamil Nadu, India
| | - Akhil Sanker
- SRM University, Kattankulathur, Tamil Nadu, India
| | - Rafal Madaj
- Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Lodz, Poland
| | | | | | - Judith Gracia
- University of Madras, Chepauk, Chennai, Tamil Nadu, India
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81
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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82
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Bajusz D, Keserű GM. Maximizing the integration of virtual and experimental screening in hit discovery. Expert Opin Drug Discov 2022; 17:629-640. [PMID: 35671403 DOI: 10.1080/17460441.2022.2085685] [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: 11/04/2022]
Abstract
INTRODUCTION Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches. AREAS COVERED After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies. EXPERT OPINION Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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83
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Luo L, Yang J, Wang C, Wu J, Li Y, Zhang X, Li H, Zhang H, Zhou Y, Lu A, Chen S. Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1123-1145. [PMID: 34705221 PMCID: PMC8548270 DOI: 10.1007/s11427-020-1959-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022]
Abstract
As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.
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Affiliation(s)
- Lu Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Jun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Cheng Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100006, China
| | - Jie Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yafang Li
- Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Xu Zhang
- weMED Health, Houston, 77054, USA
| | - Hui Li
- Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hui Zhang
- Akupunktur Akademiet, Aabyhoej, Aarhus, 8230, Denmark
| | - Yumei Zhou
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, 518033, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, China
| | - Shilin Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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84
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Tian Y, Zhang D, Cai P, Lin H, Ying H, Hu QN, Wu A. Elimination of Fusarium mycotoxin deoxynivalenol (DON) via microbial and enzymatic strategies: Current status and future perspectives. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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85
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Artificial intelligence in virtual screening: models versus experiments. Drug Discov Today 2022; 27:1913-1923. [PMID: 35597513 DOI: 10.1016/j.drudis.2022.05.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/08/2022] [Accepted: 05/12/2022] [Indexed: 12/22/2022]
Abstract
A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.
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86
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Xie W, Wang F, Li Y, Lai L, Pei J. Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models. J Chem Inf Model 2022; 62:2269-2279. [PMID: 35544331 DOI: 10.1021/acs.jcim.2c00042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.
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Affiliation(s)
- Weixin Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Fanhao Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yibo Li
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Peking-Tsinghua Center for Life Science at BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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87
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [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: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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88
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Nag S, Baidya ATK, Mandal A, Mathew AT, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12:110. [PMID: 35433167 PMCID: PMC8994527 DOI: 10.1007/s13205-022-03165-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/18/2022] [Indexed: 12/26/2022] Open
Abstract
A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.
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Affiliation(s)
- Sagorika Nag
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Anurag T. K. Baidya
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Abhimanyu Mandal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Alen T. Mathew
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bhanuranjan Das
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bharti Devi
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
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89
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Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [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: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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90
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Lu C, Liu S, Shi W, Yu J, Zhou Z, Zhang X, Lu X, Cai F, Xia N, Wang Y. Systemic evolutionary chemical space exploration for drug discovery. J Cheminform 2022; 14:19. [PMID: 35365231 PMCID: PMC8973791 DOI: 10.1186/s13321-022-00598-4] [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: 12/13/2021] [Accepted: 03/11/2022] [Indexed: 11/29/2022] Open
Abstract
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.
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Affiliation(s)
- Chong Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Shien Liu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Weihua Shi
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Jun Yu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Zhou Zhou
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Xiaoli Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Faji Cai
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Yikai Wang
- Keen Therapeutics Co., Ltd., Shanghai, China.
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91
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92
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Wang J, Bhattarai A, Do HN, Akhter S, Miao Y. Molecular Simulations and Drug Discovery of Adenosine Receptors. Molecules 2022; 27:2054. [PMID: 35408454 PMCID: PMC9000248 DOI: 10.3390/molecules27072054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 02/02/2023] Open
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.
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Affiliation(s)
| | | | | | | | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA; (J.W.); (A.B.); (H.N.D.); (S.A.)
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93
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Sha CM, Wang J, Dokholyan NV. NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules. Front Mol Biosci 2022; 9:867241. [PMID: 35392534 PMCID: PMC8980736 DOI: 10.3389/fmolb.2022.867241] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 02/22/2022] [Indexed: 01/09/2023] Open
Abstract
Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock, which accelerates the process of high-quality computational docking by a factor of 106, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy, and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 h, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.
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Affiliation(s)
- Congzhou M. Sha
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, United States
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, United States
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, United States
| | - Nikolay V. Dokholyan
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, United States
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, United States
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, United States
- Departments of Chemistry and Biomedical Engineering, Penn State University, University Park, PA, United States
- *Correspondence: Nikolay V. Dokholyan,
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94
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Drug repurposing in silico screening platforms. Biochem Soc Trans 2022; 50:747-758. [PMID: 35285479 DOI: 10.1042/bst20200967] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.
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95
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Choudhury C, Arul Murugan N, Deva Priyakumar U. Structure-based drug repurposing: traditional and advanced AI/ML-aided methods. Drug Discov Today 2022; 27:1847-1861. [PMID: 35301148 PMCID: PMC8920090 DOI: 10.1016/j.drudis.2022.03.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/16/2022] [Accepted: 03/10/2022] [Indexed: 02/08/2023]
Abstract
The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space. Teaser: This review highlights the importance of repurposable chemical space, and the contributions of conventional in silico approaches and modern machine-learning algorithms for rapid structure-based drug repurposing.
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Affiliation(s)
- Chinmayee Choudhury
- Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
| | - N Arul Murugan
- Department of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India.
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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96
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A Brief Review of Machine Learning-Based Bioactive Compound Research. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.
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97
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Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments. Pharmaceuticals (Basel) 2022; 15:ph15020236. [PMID: 35215348 PMCID: PMC8875555 DOI: 10.3390/ph15020236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.
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98
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Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, Stern A, Cherkasov A. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 2022; 17:672-697. [PMID: 35121854 DOI: 10.1038/s41596-021-00659-2] [Citation(s) in RCA: 94] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/08/2021] [Indexed: 12/14/2022]
Abstract
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.
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Affiliation(s)
- Francesco Gentile
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Jean Charle Yaacoub
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - James Gleave
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Anh-Tien Ton
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada.
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99
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Vidal-Limon A, Aguilar-Toalá JE, Liceaga AM. Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:934-943. [PMID: 34990125 DOI: 10.1021/acs.jafc.1c06110] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.
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Affiliation(s)
- Abraham Vidal-Limon
- Red de Estudios Moleculares Avanzados, Clúster Científico y Tecnológico BioMimic, Instituto de Ecología A.C. (INECOL), Carretera Antigua a Coatepec 351, El Haya, Xalapa, Veracruz 91073, Mexico
| | - José E Aguilar-Toalá
- Departamento de Ciencias de la Alimentación, División de Ciencias Biológicas y de la Salud, Universidad Autónoma Metropolitana Unidad Lerma, Avenida de las Garzas 10, Colonia El Panteón, Lerma de Villada, Estado de México 52005, Mexico
| | - Andrea M Liceaga
- Protein Chemistry and Bioactive Peptides Laboratory. Department of Food Science, Purdue University, 745 Agriculture Mall Drive, West Lafayette, Indiana 47907, United States
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100
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Ferguson AL, Brown KA. Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering. Annu Rev Chem Biomol Eng 2022; 13:25-44. [PMID: 35236085 DOI: 10.1146/annurev-chembioeng-092120-020803] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and engineering of molecules and molecular systems have long been a preoccupation of chemical and biomolecular engineers using a variety of computational and experimental techniques. Increasingly, researchers have looked to emerging and established tools in artificial intelligence and machine learning to integrate with established approaches in chemical science to realize powerful, efficient, and in some cases autonomous platforms for molecular discovery, materials engineering, and process optimization. This review summarizes the basic principles underpinning these techniques and highlights recent successful example applications in autonomous materials discovery, transfer learning, and multi-fidelity active learning. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA;
| | - Keith A Brown
- Mechanical Engineering, Boston University, Boston, Massachusetts 02215, USA;
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