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Ancajas CMF, Oyedele AS, Butt CM, Walker AS. Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products. Nat Prod Rep 2024. [PMID: 38912779 DOI: 10.1039/d4np00009a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.
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Affiliation(s)
| | | | - Caitlin M Butt
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
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2
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Alberga D, Lamanna G, Graziano G, Delre P, Lomuscio MC, Corriero N, Ligresti A, Siliqi D, Saviano M, Contino M, Stefanachi A, Mangiatordi GF. DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation. Comput Biol Med 2024; 175:108486. [PMID: 38653065 DOI: 10.1016/j.compbiomed.2024.108486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.
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Affiliation(s)
- Domenico Alberga
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giuseppe Lamanna
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giovanni Graziano
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Pietro Delre
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | | | - Nicola Corriero
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Alessia Ligresti
- CNR - Institute of Biomolecular Chemistry, Via Campi Flegrei 34, 80078, Pozzuoli, Italy
| | - Dritan Siliqi
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Michele Saviano
- CNR - Institute of Crystallography, Via Vivaldi 43, 81100, Caserta, Italy
| | - Marialessandra Contino
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Angela Stefanachi
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
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3
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Gou R, Yang J, Guo M, Chen Y, Xue W. CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design. J Chem Inf Model 2024; 64:4059-4070. [PMID: 38739718 DOI: 10.1021/acs.jcim.4c00504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.
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Affiliation(s)
- Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jingyi Yang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Menghan Guo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yingjun Chen
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Shen T, Li S, Wang XS, Wang D, Wu S, Xia J, Zhang L. Deep reinforcement learning enables better bias control in benchmark for virtual screening. Comput Biol Med 2024; 171:108165. [PMID: 38402838 DOI: 10.1016/j.compbiomed.2024.108165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.
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Affiliation(s)
- Tao Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Shan Li
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, USA
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
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Panwar U, Murali A, Khan MA, Selvaraj C, Singh SK. Virtual Screening Process: A Guide in Modern Drug Designing. Methods Mol Biol 2024; 2714:21-31. [PMID: 37676591 DOI: 10.1007/978-1-0716-3441-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.
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Affiliation(s)
- Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Aarthy Murali
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Mohammad Aqueel Khan
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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Kaboudi N, Krüger N, Hamzeh-Mivehroud M. Development of novel ligands against SARS-CoV-2 M pro enzyme: an in silico and in vitro Study. Mol Inform 2023; 42:e202300120. [PMID: 37590494 DOI: 10.1002/minf.202300120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/22/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND Despite tremendous efforts made by scientific community during the outbreak of COVID-19 pandemic, this disease still remains as a public health concern. Although different types of vaccines were globally used to reduce the mortality, emergence of new variants of SARS-CoV-2 is a challenging issue in COVID-19 pharmacotherapy. In this context, target therapy of SARS-CoV-2 by small ligands is a promising strategy. METHODS In this investigation, we applied ligand-based virtual screening for finding novel molecules based on nirmatrelvir structure. Various criteria including drug-likeness, ADME, and toxicity properties were applied for filtering the compounds. The selected candidate molecules were subjected to molecular docking and dynamics simulation for predicting the binding mode and binding free energy, respectively. Then the molecules were experimentally evaluated in terms of antiviral activity against SARS-CoV-2 and toxicity assessment. RESULTS The results demonstrated that the identified compounds showed inhibitory activity towards SARS-CoV-2 Mpro . CONCLUSION In summary, the introduced compounds may provide novel scaffold for further structural modification and optimization with improved anti SARS-CoV-2 Mpro activity.
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Affiliation(s)
- Navid Kaboudi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nadine Krüger
- Platform Infection Models, German Primate Center-Leibniz Institute for Primate Research, 37077, Göttingen, Germany
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Khan M, Kandwal S, Fayne D. DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design. ACS OMEGA 2023; 8:39468-39480. [PMID: 37901539 PMCID: PMC10601415 DOI: 10.1021/acsomega.3c05207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023]
Abstract
With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.
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Affiliation(s)
- Mohemmed
Faraz Khan
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
- Department
of Pharmaceutical Chemistry, Faculty of Pharmacy, Integral University, Lucknow U.P., 226026, India
| | - Shubhangi Kandwal
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Darren Fayne
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
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Wu K, Karapetyan E, Schloss J, Vadgama J, Wu Y. Advancements in small molecule drug design: A structural perspective. Drug Discov Today 2023; 28:103730. [PMID: 37536390 PMCID: PMC10543554 DOI: 10.1016/j.drudis.2023.103730] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 08/05/2023]
Abstract
In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.
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Affiliation(s)
- Ke Wu
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA
| | - Eduard Karapetyan
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA
| | - John Schloss
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA; School of Pharmacy, American University of Health Sciences, Signal Hill, CA 90755, USA
| | - Jaydutt Vadgama
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA; School of Pharmacy, American University of Health Sciences, Signal Hill, CA 90755, USA.
| | - Yong Wu
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA.
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Nguyen TH, Wang SL, Nguyen VB. Microorganism-Derived Molecules as Enzyme Inhibitors to Target Alzheimer's Diseases Pathways. Pharmaceuticals (Basel) 2023; 16:ph16040580. [PMID: 37111337 PMCID: PMC10146315 DOI: 10.3390/ph16040580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. It increases the risk of other serious diseases and causes a huge impact on individuals, families, and socioeconomics. AD is a complex multifactorial disease, and current pharmacological therapies are largely based on the inhibition of enzymes involved in the pathogenesis of AD. Natural enzyme inhibitors are the potential sources for targeting AD treatment and are mainly collected from plants, marine organisms, or microorganisms. In particular, microbial sources have many advantages compared to other sources. While several reviews on AD have been reported, most of these previous reviews focused on presenting and discussing the general theory of AD or overviewing enzyme inhibitors from various sources, such as chemical synthesis, plants, and marine organisms, while only a few reviews regarding microbial sources of enzyme inhibitors against AD are available. Currently, multi-targeted drug investigation is a new trend for the potential treatment of AD. However, there is no review that has comprehensively discussed the various kinds of enzyme inhibitors from the microbial source. This review extensively addresses the above-mentioned aspect and simultaneously updates and provides a more comprehensive view of the enzyme targets involved in the pathogenesis of AD. The emerging trend of using in silico studies to discover drugs concerning AD inhibitors from microorganisms and perspectives for further experimental studies are also covered here.
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Affiliation(s)
- Thi Hanh Nguyen
- Doctoral Program in Applied Sciences, Tamkang University, New Taipei City 25137, Taiwan
- Department of Chemistry, Tamkang University, New Taipei City 25137, Taiwan
| | - San-Lang Wang
- Department of Chemistry, Tamkang University, New Taipei City 25137, Taiwan
| | - Van Bon Nguyen
- Institute of Biotechnology and Environment, Tay Nguyen University, Buon Ma Thuot 630000, Vietnam
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10
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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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11
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Lauritano C, Montuori E, De Falco G, Carrella S. In Silico Methodologies to Improve Antioxidants' Characterization from Marine Organisms. Antioxidants (Basel) 2023; 12:710. [PMID: 36978958 PMCID: PMC10045275 DOI: 10.3390/antiox12030710] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/02/2023] [Accepted: 03/07/2023] [Indexed: 03/17/2023] Open
Abstract
Marine organisms have been reported to be valuable sources of bioactive molecules that have found applications in different industrial fields. From organism sampling to the identification and bioactivity characterization of a specific compound, different steps are necessary, which are time- and cost-consuming. Thanks to the advent of the -omic era, numerous genome, metagenome, transcriptome, metatranscriptome, proteome and microbiome data have been reported and deposited in public databases. These advancements have been fundamental for the development of in silico strategies for basic and applied research. In silico studies represent a convenient and efficient approach to the bioactivity prediction of known and newly identified marine molecules, reducing the time and costs of "wet-lab" experiments. This review focuses on in silico approaches applied to bioactive molecule discoveries from marine organisms. When available, validation studies reporting a bioactivity assay to confirm the presence of an antioxidant molecule or enzyme are reported, as well. Overall, this review suggests that in silico approaches can offer a valuable alternative to most expensive approaches and proposes them as a little explored field in which to invest.
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Affiliation(s)
- Chiara Lauritano
- Ecosustainable Marine Biotechnology Department, Stazione Zoologica Anton Dohrn, Via Acton 55, 80133 Napoli, Italy
| | - Eleonora Montuori
- Ecosustainable Marine Biotechnology Department, Stazione Zoologica Anton Dohrn, Via Acton 55, 80133 Napoli, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale F. Stagno d’Alcontres 31, 98166 Messina, Italy
| | - Gabriele De Falco
- Ecosustainable Marine Biotechnology Department, Stazione Zoologica Anton Dohrn, Via Acton 55, 80133 Napoli, Italy
| | - Sabrina Carrella
- Ecosustainable Marine Biotechnology Department, Stazione Zoologica Anton Dohrn, Via Acton 55, 80133 Napoli, Italy
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12
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Mensa S, Sahin E, Tacchino F, Kl Barkoutsos P, Tavernelli I. Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1088/2632-2153/acb900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Abstract
Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.
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13
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Wang Z, Zheng L, Wang S, Lin M, Wang Z, Kong AWK, Mu Y, Wei Y, Li W. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. Brief Bioinform 2023; 24:6887112. [PMID: 36502369 DOI: 10.1093/bib/bbac520] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/17/2022] [Accepted: 10/31/2022] [Indexed: 12/14/2022] Open
Abstract
The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.
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Affiliation(s)
- Zechen Wang
- School of Physics, Shandong University, Jinan, Shandong 250100, China
| | - Liangzhen Zheng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.,Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Sheng Wang
- Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Mingzhi Lin
- Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Zhihao Wang
- School of Physics, Shandong University, Jinan, Shandong 250100, China
| | - Adams Wai-Kin Kong
- Rolls-Royce Corporate Lab, Nanyang Technological University, Singapore 637551, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Yanjie Wei
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Weifeng Li
- School of Physics, Shandong University, Jinan, Shandong 250100, China
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14
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Kapoor S, Chatterjee DR, Chowdhury MG, Das R, Shard A. Roadmap to Pyruvate Kinase M2 Modulation - A Computational Chronicle. Curr Drug Targets 2023; 24:464-483. [PMID: 36998144 DOI: 10.2174/1389450124666230330103126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/14/2023] [Accepted: 02/10/2023] [Indexed: 04/01/2023]
Abstract
Pyruvate kinase M2 (PKM2) has surfaced as a potential target for anti-cancer therapy. PKM2 is known to be overexpressed in the tumor cells and is a critical metabolic conduit in supplying the augmented bioenergetic demands of the recalcitrant cancer cells. The presence of PKM2 in structurally diverse tetrameric as well as dimeric forms has opened new avenues to design novel modulators. It is also a truism to state that drug discovery has advanced significantly from various computational techniques like molecular docking, virtual screening, molecular dynamics, and pharmacophore mapping. The present review focuses on the role of computational tools in exploring novel modulators of PKM2. The structural features of various isoforms of PKM2 have been discussed along with reported modulators. An extensive analysis of the structure-based and ligand- based in silico methods aimed at PKM2 modulation has been conducted with an in-depth review of the literature. The role of advanced tools like QSAR and quantum mechanics has been established with a brief discussion of future perspectives.
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Affiliation(s)
- Saumya Kapoor
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Deep Rohan Chatterjee
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Moumita Ghosh Chowdhury
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Rudradip Das
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Amit Shard
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
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15
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He J, Li C, Hu W, Li C, Liu S, Sui J, Zhang T, Sun Q, Luo Y. Identification of selective mtbDHFR inhibitors by virtual screening and experimental approaches. Chem Biol Drug Des 2022; 100:1005-1016. [PMID: 34981654 DOI: 10.1111/cbdd.14018] [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] [Received: 08/31/2021] [Revised: 11/26/2021] [Accepted: 12/18/2021] [Indexed: 02/05/2023]
Abstract
mtbDHFR-targeting inhibition has become a promising approach for tuberculosis treatment. In the current research, a multi-step virtual screening effort toward ZINC and MCE databases was devoted to discover novel mtbDHFR inhibitors. Based on binding affinity of small molecules through molecular docking study in AutoDock Vina, the number of compounds was reduced to 952,688. Further, these compounds were employed by a step-by-step multiple docking programs of Schrödinger suite and filtered by pharmacokinetics and PAINS parameters. Finally, nine ZINC compounds and 400 MCE compounds were obtained. These compounds of binding ability were tested with mtbDHFR by FluoPol-ABPP approach established in this work. Finally, AF-353 compound was found to have strong binding effect to mtbDHFR. AF-353 was further tested for mtb and hDHFR enzymatic activities, and it was proved to possess 50-fold selectivity toward mtbDHFR over hDHFR. In silico MD simulation results supported this selectivity.
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Affiliation(s)
- Juan He
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Cong Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Wei Hu
- Chengdu FenDi pharmaceutical Co., Ltd., Chengdu, China
| | - Chungen Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Song Liu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Jing Sui
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Tianyu Zhang
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health (GIBH), Chinese Academy of Sciences (CAS), Guangzhou, China
| | - Qingxiang Sun
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Youfu Luo
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
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16
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Sauer S, Matter H, Hessler G, Grebner C. Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods. Front Chem 2022; 10:1012507. [PMID: 36339033 PMCID: PMC9629386 DOI: 10.3389/fchem.2022.1012507] [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: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/14/2022] Open
Abstract
The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively.
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17
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New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:pharmaceutics14091914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [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: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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18
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Wang G, Bai Y, Cui J, Zong Z, Gao Y, Zheng Z. Computer-Aided Drug Design Boosts RAS Inhibitor Discovery. Molecules 2022; 27:molecules27175710. [PMID: 36080477 PMCID: PMC9457765 DOI: 10.3390/molecules27175710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/13/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.
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Affiliation(s)
- Ge Wang
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuhao Bai
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Jiarui Cui
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zirui Zong
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuan Gao
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zhen Zheng
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Correspondence:
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19
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Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies. Int J Mol Sci 2022; 23:ijms23169374. [PMID: 36012627 PMCID: PMC9409045 DOI: 10.3390/ijms23169374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/17/2022] Open
Abstract
Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.
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20
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Petrović D, Scott JS, Bodnarchuk MS, Lorthioir O, Boyd S, Hughes GM, Lane J, Wu A, Hargreaves D, Robinson J, Sadowski J. Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors. J Chem Inf Model 2022; 62:3832-3843. [PMID: 35920716 DOI: 10.1021/acs.jcim.2c00644] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
ROS1 rearrangements account for 1-2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.
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Affiliation(s)
- Dušan Petrović
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 431 50, Sweden
| | - James S Scott
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | | | | | - Scott Boyd
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - George M Hughes
- Discovery Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Jordan Lane
- Discovery Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Allan Wu
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
| | - David Hargreaves
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - James Robinson
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Jens Sadowski
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 431 50, Sweden
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21
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CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding. Nat Rev Chem 2022; 6:287-295. [PMID: 35783295 PMCID: PMC9246350 DOI: 10.1038/s41570-022-00363-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.
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22
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In Silico Drug Repurposing Approach: Investigation of Mycobacterium tuberculosis FadD32 Targeted by FDA-Approved Drugs. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27030668. [PMID: 35163931 PMCID: PMC8840176 DOI: 10.3390/molecules27030668] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/27/2021] [Accepted: 01/09/2022] [Indexed: 12/17/2022]
Abstract
Background: Despite the enormous efforts made towards combating tuberculosis (TB), the disease remains a major global threat. Hence, new drugs with novel mechanisms against TB are urgently needed. Fatty acid degradation protein D32 (FadD32) has been identified as a promising drug target against TB, the protein is required for the biosynthesis of mycolic acids, hence, essential for the growth and multiplication of the mycobacterium. However, the FadD32 mechanism upon the binding of FDA-approved drugs is not well established. Herein, we applied virtual screening (VS), molecular docking, and molecular dynamic (MD) simulation to identify potential FDA-approved drugs against FadD32. Methodology/Results: VS technique was found promising to identify four FDA-approved drugs (accolate, sorafenib, mefloquine, and loperamide) with higher molecular docking scores, ranging from -8.0 to -10.0 kcal/mol. Post-MD analysis showed that the accolate hit displayed the highest total binding energy of -45.13 kcal/mol. Results also showed that the accolate hit formed more interactions with FadD32 active site residues and all active site residues displayed an increase in total binding contribution. RMSD, RMSF, Rg, and DCCM analysis further supported that the presence of accolate exhibited more structural stability, lower bimolecular flexibility, and more compactness into the FadD32 protein. Conclusions: Our study revealed accolate as the best potential drug against FadD32, hence a prospective anti-TB drug in TB therapy. In addition, we believe that the approach presented in the current study will serve as a cornerstone to identifying new potential inhibitors against a wide range of biological targets.
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23
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Abstract
Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.
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24
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Bayarri G, Hospital A, Orozco M. 3dRS, a Web-Based Tool to Share Interactive Representations of 3D Biomolecular Structures and Molecular Dynamics Trajectories. Front Mol Biosci 2021; 8:726232. [PMID: 34485386 PMCID: PMC8414788 DOI: 10.3389/fmolb.2021.726232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/03/2021] [Indexed: 11/13/2022] Open
Abstract
3D Representation Sharing (3dRS) is a web-based tool designed to share biomolecular structure representations, including 4D ensembles derived from Molecular Dynamics (MD) trajectories. The server offers a team working in different locations a single URL to share and discuss structural data in an interactive fashion, with the possibility to use it as a live figure for scientific papers. The web tool allows an easy upload of structures and trajectories in different formats. The 3D representation, powered by NGL viewer, offers an interactive display with smooth visualization in modern web browsers. Multiple structures can be loaded and superposed in the same scene. 1D sequences from the loaded structures are presented and linked to the 3D representation. Multiple, pre-defined 3D molecular representations are available. The powerful NGL selection syntax allows the definition of molecular regions that can be then displayed using different representations. Important descriptors such as distances or interactions can be easily added into the representation. Trajectory frames can be explored using a common video player control panel. Trajectories are efficiently stored and transferred to the NGL viewer thanks to an MDsrv-based data streaming. The server design offers all functionalities in one single web page, with a curated user experience, involving a minimum learning curve. Extended documentation is available, including a gallery with a collection of scenes. The server requires no registration and is available at https://mmb.irbbarcelona.org/3dRS.
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Affiliation(s)
- Genís Bayarri
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Departament de Bioquímica i Biomedicina, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
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25
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Gossen J, Albani S, Hanke A, Joseph BP, Bergh C, Kuzikov M, Costanzi E, Manelfi C, Storici P, Gribbon P, Beccari AR, Talarico C, Spyrakis F, Lindahl E, Zaliani A, Carloni P, Wade RC, Musiani F, Kokh DB, Rossetti G. A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics. ACS Pharmacol Transl Sci 2021; 4:1079-1095. [PMID: 34136757 PMCID: PMC8009102 DOI: 10.1021/acsptsci.0c00215] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Indexed: 12/27/2022]
Abstract
The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.
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Affiliation(s)
- Jonas Gossen
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Simone Albani
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Anton Hanke
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
- Institute
of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Im Neuenheimer Feld 364, Heidelberg, 69120, Germany
| | - Benjamin P. Joseph
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Cathrine Bergh
- Science for
Life Laboratory & Swedish e-Science Research Center, Department
of Applied Physics, KTH Royal Institute
of Technology, Stockholm, 11428, Sweden
| | - Maria Kuzikov
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | - Elisa Costanzi
- Elettra-Sincrotrone
Trieste S.C.p.A., SS 14-km 163,5 in AREA Science Park, Basovizza,
Trieste, 34149, Italy
| | - Candida Manelfi
- Dompé
Farmaceutici SpA, Via Campo di Pile, L’Aquila, 67100, Italy
| | - Paola Storici
- Elettra-Sincrotrone
Trieste S.C.p.A., SS 14-km 163,5 in AREA Science Park, Basovizza,
Trieste, 34149, Italy
| | - Philip Gribbon
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | | | - Carmine Talarico
- Dompé
Farmaceutici SpA, Via Campo di Pile, L’Aquila, 67100, Italy
| | - Francesca Spyrakis
- Department
of Drug Science and Technology, University
of Turin, via Giuria
9, Turin, 10125, Italy
| | - Erik Lindahl
- Science for
Life Laboratory & Swedish e-Science Research Center, Department
of Applied Physics, KTH Royal Institute
of Technology, Stockholm, 11428, Sweden
- Science
for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-106 91, Sweden
| | - Andrea Zaliani
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | - Paolo Carloni
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Rebecca C. Wade
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
- Zentrum
für Molekulare Biologie der University Heidelberg, DKFZ-ZMBH
Alliance, INF 282, Heidelberg, 69120, Germany
- Interdisciplinary
Center for Scientific Computing (IWR), Heidelberg
University, INF 368, Heidelberg, 69120, Germany
| | - Francesco Musiani
- Laboratory
of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Daria B. Kokh
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
| | - Giulia Rossetti
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Jülich
Supercomputing Center (JSC), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Department
of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University, Aachen, 44517, Germany
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26
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Fischer A, Smieško M, Sellner M, Lill MA. Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results. J Med Chem 2021; 64:2489-2500. [PMID: 33617246 DOI: 10.1021/acs.jmedchem.0c02227] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.
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Affiliation(s)
- André Fischer
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Manuel Sellner
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Markus A Lill
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
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