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Tamang JSD, Banerjee S, Baidya SK, Ghosh B, Adhikari N, Jha T. Employing comparative QSAR techniques for the recognition of dibenzofuran and dibenzothiophene derivatives toward MMP-12 inhibition. J Biomol Struct Dyn 2024; 42:7304-7320. [PMID: 37498149 DOI: 10.1080/07391102.2023.2239923] [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: 04/26/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
Among various matrix metalloproteinases (MMPs), MMP-12 is one of the potential targets for cancer and other diseases. However, none of the MMP-12 inhibitors has passed the clinical trials to date. Therefore, designing potential MMP-12 inhibitors as new drug molecules can provide effective therapeutic strategies for several diseases. In this study, a series of dibenzofuran and dibenzothiophene derivatives were subjected to different 2D and 3D-QSAR techniques to point out the crucial structural contributions highly influential toward the MMP-12 inhibitory activity. These techniques identified some structural attributes of these compounds that are responsible for influencing their MMP-12 inhibition. The carboxylic group may enhance proper binding with catalytic Zn2+ ion at the MMP-12 active site. Again, the i-propyl sulfonamido carboxylic acid function contributed positively toward MMP-12 inhibition. Moreover, the dibenzofuran moiety conferred stable binding at the S1' pocket for higher MMP-12 inhibition. The steric and hydrophobic groups were found favourable near the furan ring substituted at the dibenzofuran moiety. Besides these ligand-based approaches, molecular docking and molecular dynamic (MD) simulation studies not only elucidated the importance of several aspects of these MMP-12 inhibitors while disclosing the significance of the finding of these QSAR studies and their influences toward MMP-12 inhibition. The MD simulation study also revealed stable and compact binding between such compounds at the MMP-12 active site. Therefore, the findings of these validated ligand-based and structure-based molecular modeling studies can aid the development of selective and potent lead molecules that can be used for the treatment of MMP-12-associated diseases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Jigme Sangay Dorjay Tamang
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Sandip Kumar Baidya
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Balaram Ghosh
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, India
| | - Nilanjan Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Salvatti BA, Chagas MA, Fernandes PO, Ladeira YFX, Bozzi AS, Valadares VS, Valente AP, de Miranda AS, Rocha WR, Maltarollo VG, Moraes AH. Understanding the Enzyme ( S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones. J Chem Inf Model 2024; 64:4462-4474. [PMID: 38776464 DOI: 10.1021/acs.jcim.3c01773] [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: 05/25/2024]
Abstract
The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.
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Affiliation(s)
- Brunno A Salvatti
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Marcelo A Chagas
- Departamento de Ciências Exatas, Universidade do Estado de Minas Gerais, João Monlevade, Minas Gerais 35930-314, Brazil
| | - Phillipe O Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Yan F X Ladeira
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Aline S Bozzi
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Veronica S Valadares
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Ana Paula Valente
- Centro Nacional de Ressonância Magnética Nuclear, Instituto de Bioquímica Médica Leopoldo de Meis, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21.941-902, Brazil
| | - Amanda S de Miranda
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Willian R Rocha
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Vinicius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Adolfo H Moraes
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
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3
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Serafim MSM, Kronenberger T, Rocha REO, Rosa ADRA, Mello TLG, Poso A, Ferreira RS, Abrahão JS, Kroon EG, Mota BEF, Maltarollo VG. Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach. J Chem Inf Model 2024; 64:393-411. [PMID: 38194508 DOI: 10.1021/acs.jcim.3c01505] [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: 01/11/2024]
Abstract
Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Thales Kronenberger
- Institute of Pharmacy, Pharmaceutical/Medicinal Chemistry and Tübingen Center for Academic Drug Discovery (TüCAD2), Eberhard Karls University Tübingen, Auf der Morgenstelle 8, Tübingen 72076, Germany
- Excellence Cluster "Controlling Microbes to Fight Infections" (CMFI), Tübingen 72076, Germany
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio 70211, Finland
| | - Rafael Eduardo Oliveira Rocha
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Amanda Del Rio Abreu Rosa
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Thaysa Lara Gonçalves Mello
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Antti Poso
- Institute of Pharmacy, Pharmaceutical/Medicinal Chemistry and Tübingen Center for Academic Drug Discovery (TüCAD2), Eberhard Karls University Tübingen, Auf der Morgenstelle 8, Tübingen 72076, Germany
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio 70211, Finland
- Department of Medical Oncology and Pneumology, University Hospital of Tübingen, Tübingen 70211, Germany
| | - Rafaela Salgado Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Jonatas Santos Abrahão
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Erna Geessien Kroon
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG 31270-901, Brazil
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Oliveira NJC, Dos Santos Júnior VS, Pierotte IC, Leocádio VAT, Santana LFDA, Marques GVDL, Protti ÍF, Braga SFP, Kohlhoff M, Freitas TR, Sabino ADP, Kronenberger T, Gonçalves JE, Johann S, Santos DA, César IDC, Maltarollo VG, Oliveira RB. Discovery of Lead 2-Thiazolylhydrazones with Broad-Spectrum and Potent Antifungal Activity. J Med Chem 2023; 66:16628-16645. [PMID: 38064359 DOI: 10.1021/acs.jmedchem.3c01105] [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: 12/29/2023]
Abstract
Opportunistic fungal infections represent a global health problem, mainly for immunocompromised individuals. New therapeutical options are needed since several fungal strains show resistance to clinically available antifungal agents. 2-Thiazolylhydrazones are well-known as potent compounds against Candida and Cryptococcus species. A scaffold-focused drug design using machine-learning models was established to optimize the 2-thiazolylhydrazone skeleton and obtain novel compounds with higher potency, better solubility in water, and enhanced absorption. Twenty-nine novel compounds were obtained and most showed low micromolar MIC values against different species of Candida and Cryptococcus spp., including Candida auris, an emerging multidrug-resistant yeast. Among the synthesized compounds, 2-thiazolylhydrazone 28 (MIC value ranging from 0.8 to 52.17 μM) was selected for further studies: cytotoxicity evaluation, permeability study in Caco-2 cell model, and in vivo efficacy against Cryptococcus neoformans in an invertebrate infection model. All results obtained indicate the great potential of 28 as a novel antifungal agent.
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Affiliation(s)
- Nereu Junio Cândido Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Valtair Severino Dos Santos Júnior
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Isabella Campolina Pierotte
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Victor Augusto Teixeira Leocádio
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Luiz Felipe de Andrade Santana
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Gabriel Vitor de Lima Marques
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Ícaro Ferrari Protti
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Saulo Fehelberg Pinto Braga
- Departamento de Farmácia, Escola de Farmácia, Universidade Federal de Ouro Preto, Ouro Preto, Minas Gerais 35400-000, Brazil
| | - Markus Kohlhoff
- Química de Produtos Naturais Bioativos (QPNB), Instituto René Rachou (IRR) - FIOCRUZ Minas, Belo Horizonte 30190-009, Brazil
| | - Túlio Resende Freitas
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Adriano de Paula Sabino
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Thales Kronenberger
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard-Karls-Universität, Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
- Tuebingen Center for Academic Drug Discovery & Development (TüCAD2), 72076 Tuebingen, Germany
- Excellence Cluster ″Controlling Microbes to Fight Infections″ (CMFI), 72076 Tübingen, Germany
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - José Eduardo Gonçalves
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Susana Johann
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Daniel A Santos
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Isabela da Costa César
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Renata Barbosa Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
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Veríssimo GC, Pantaleão SQ, Fernandes PDO, Gertrudes JC, Kronenberger T, Honorio KM, Maltarollo VG. MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling. J Comput Aided Mol Des 2023; 37:735-754. [PMID: 37804393 DOI: 10.1007/s10822-023-00536-y] [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: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023]
Abstract
QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.
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Affiliation(s)
- Gabriel Corrêa Veríssimo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | | | - Philipe de Olveira Fernandes
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | - Jadson Castro Gertrudes
- Department of Computing, Institute of Exact and Biological Sciences, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil
| | - Thales Kronenberger
- Department of Pharmaceutical and Medicinal Chemistry, University of Tübingen, Tübingen, BW, 72076, Germany
| | - Kathia Maria Honorio
- Federal University of ABC, Santo André, SP, 09210-170, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, SP, 03828-000, Brazil
| | - Vinícius Gonçalves Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil.
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6
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Fernandes PDO, Martins JPA, de Melo EB, de Oliveira RB, Kronenberger T, Maltarollo VG. Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti- Cryptococcus neoformans activity. J Biomol Struct Dyn 2022; 40:9789-9800. [PMID: 34121616 DOI: 10.1080/07391102.2021.1935321] [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: 12/16/2022]
Abstract
Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r2 for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Philipe de Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - João Paulo A Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Eduardo B de Melo
- Laboratório de Química Medicinal e Ambiental Teórica, Universidade Estadual do Oeste do Paraná, Cascavel, Paraná, Brazil
| | - Renata Barbosa de Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Thales Kronenberger
- Department of Pneumonology and Oncology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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7
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Feng D, Baumgartner R. A closer look at the kernels generated by the decision and regression tree ensembles. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2150680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Dai Feng
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
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8
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de Padua RM, Kratz JM, Munkert J, Bertol JW, Rigotto C, Schuster D, Maltarollo VG, Kreis W, Simões CMO, Braga F. Effects of Lipophilicity and Structural Features on the Antiherpes Activity of Digitalis Cardenolides and Derivatives. Chem Biodivers 2022; 19:e202200411. [DOI: 10.1002/cbdv.202200411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/06/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Rodrigo Maia de Padua
- UFMG: Universidade Federal de Minas Gerais Pharmaceutical Products Av. Antônio Carlos 6627 Belo Horizonte BRAZIL
| | - Jadel Müller Kratz
- UFSC: Universidade Federal de Santa Catarina Pharmaceutical Sciences R. Delfino Conti, S/N Florianópolis BRAZIL
| | - Jennifer Munkert
- University of Erlangen-Nuernberg: Friedrich-Alexander-Universitat Erlangen-Nurnberg Division of Pharmaceutical Biology Staudtstraße 5 Erlangen GERMANY
| | - Jéssica Wildgrube Bertol
- UFSC: Universidade Federal de Santa Catarina Pharmaceutical Sciences R. Delfino Conti, S/N Florianópolis BRAZIL
| | - Caroline Rigotto
- UFSC: Universidade Federal de Santa Catarina Pharmaceutical Sciences R. Delfino Conti, S/N Florianópolis BRAZIL
| | - Daniela Schuster
- Paracelsus Medical University Salzburg: Paracelsus Medizinische Privatuniversitat Department of Pharmaceutical and Medicinal Chemistry Strubergasse 21 Salzburg AUSTRIA
| | | | - Wolfgang Kreis
- University of Erlangen-Nuernberg: Friedrich-Alexander-Universitat Erlangen-Nurnberg Division of Pharmaceutical Biology Staudtstraße 5 Erlangen GERMANY
| | | | - Fernão Braga
- Universidade Federal de Minas Gerais Pharmaceutical Sciences Av. Antônio Carlos 6627 31270901 Belo Horizonte BRAZIL
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9
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Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
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Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
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10
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Azevedo L, Serafim MSM, Maltarollo VG, Grabrucker AM, Granato D. Atherosclerosis fate in the era of tailored functional foods: Evidence-based guidelines elicited from structure- and ligand-based approaches. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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Hayashi Y, Nakano Y, Marumo Y, Kumada S, Okada K, Onuki Y. Application of machine learning to a material library for modeling of relationships between material properties and tablet properties. Int J Pharm 2021; 609:121158. [PMID: 34624447 DOI: 10.1016/j.ijpharm.2021.121158] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 10/20/2022]
Abstract
This study investigates the usefulness of machine learning for modeling complex relationships in a material library. We tested 81 types of active pharmaceutical ingredients (APIs) and their tablets to construct the library, which included the following variables: 20 types of API material properties, one type of process parameter (three levels of compression pressure), and two types of tablet properties (tensile strength (TS) and disintegration time (DT)). The machine learning algorithms boosted tree (BT) and random forest (RF) were applied to analysis of our material library to model the relationships between input variables (material properties and compression pressure) and output variables (TS and DT). The calculated BT and RF models achieved higher performance statistics compared with a conventional modeling method (i.e., partial least squares regression), and revealed the material properties that strongly influence TS and DT. For TS, true density, the tenth percentile of the cumulative percentage size distribution, loss on drying, and compression pressure were of high relative importance. For DT, total surface energy, water absorption rate, polar surface energy, and hygroscopicity had significant effects. Thus, we demonstrate that BT and RF can be used to model complex relationships and clarify important material properties in a material library.
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Affiliation(s)
- Yoshihiro Hayashi
- Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa, Namerikawa-shi, Toyama 936-0857, Japan; Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan.
| | - Yuri Nakano
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan
| | - Yuki Marumo
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan
| | - Shungo Kumada
- Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa, Namerikawa-shi, Toyama 936-0857, Japan
| | - Kotaro Okada
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan
| | - Yoshinori Onuki
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan
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12
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He S, Leanse LG, Feng Y. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Adv Drug Deliv Rev 2021; 178:113922. [PMID: 34461198 DOI: 10.1016/j.addr.2021.113922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/23/2022]
Abstract
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.
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Affiliation(s)
- Sheng He
- Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Leon G Leanse
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Yanfang Feng
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
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13
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Agyapong O, Miller WA, Wilson MD, Kwofie SK. Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors. Mol Divers 2021; 26:2231-2242. [PMID: 34626303 DOI: 10.1007/s11030-021-10329-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 09/23/2021] [Indexed: 11/26/2022]
Abstract
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .
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Affiliation(s)
- Odame Agyapong
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, PMB LG 77, Legon, Accra, Ghana
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, P.O. Box LG 581, Legon, Accra, Ghana
| | - Whelton A Miller
- Department of Medicine, Loyola University Medical Center, Maywood, IL, 60153, USA
- School of Engineering and Applied Science, Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL, 60153, USA
| | - Michael D Wilson
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, P.O. Box LG 581, Legon, Accra, Ghana
- Department of Medicine, Loyola University Medical Center, Maywood, IL, 60153, USA
| | - Samuel K Kwofie
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, PMB LG 77, Legon, Accra, Ghana.
- West African Centre for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana.
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14
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Luo D, Tong JB, Zhang X, Xiao XC, Bian S. Computational strategies towards developing novel SARS-CoV-2 M pro inhibitors against COVID-19. J Mol Struct 2021; 1247:131378. [PMID: 34483363 PMCID: PMC8398673 DOI: 10.1016/j.molstruc.2021.131378] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 11/25/2022]
Abstract
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 Mpro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 Mpro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 Mpro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.
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Affiliation(s)
- Ding Luo
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Jian-Bo Tong
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Xing Zhang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Xue-Chun Xiao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Shuai Bian
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
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15
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Fernandes PO, Martins DM, de Souza Bozzi A, Martins JPA, de Moraes AH, Maltarollo VG. Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking. Mol Divers 2021; 25:1301-1314. [PMID: 34191245 PMCID: PMC8241884 DOI: 10.1007/s11030-021-10261-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022]
Abstract
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
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Affiliation(s)
- Philipe Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Diego Magno Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Aline de Souza Bozzi
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - João Paulo A Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Adolfo Henrique de Moraes
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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16
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Molecular modeling studies of [4-(3 H-benzoimidazol-5-yl)-pyrimidin-2-yl]-amine-based CDK4 inhibitors. Future Med Chem 2021; 13:1317-1339. [PMID: 34210159 DOI: 10.4155/fmc-2020-0393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: CDK4 is a promising target for breast cancer therapy. This study aimed to explore the structure-activity relationship of CDK4 inhibitor abemaciclib analogs and design potent CDK4 inhibitors for breast cancer treatment. Methods & results: A faithful 3D quantitative structure-activity relationship model was established by molecular docking, comparative molecular field analysis and comparative molecular similarity index analysis based on 56 abemaciclib analogs. Molecular dynamics simulation studies revealed the key residues of the interaction between CDK4 and inhibitors. Four novel inhibitors with satisfactory predicted binding affinity to CDK4 were designed. Conclusion: The 3D quantitative structure-activity relationship and molecular dynamics simulation studies provide valuable insight into the development of novel CDK4 inhibitors.
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17
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Zhang H, Shen C, Zhang HR, Chen WX, Luo QQ, Ding L. Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model. Mol Divers 2021; 25:1481-1495. [PMID: 34160713 DOI: 10.1007/s11030-021-10247-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023]
Abstract
DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discovery. In this investigation, the multistep virtual screening methods, including machine learning methods and common feature pharmacophore model, were developed and used to identify novel DGAT1 inhibitors from BioDiversity database with 30,000 compounds. 531 compounds were predicted as DGAT1 inhibitors by combination of machine learning methods comprising of SVM, NB and RP models. Then, 12 agents were filtered from 531 compounds by using the common feature pharmacophore model. The 3D chemical structures of the 12 hits coordinated with surface charges and isosurface have been carefully analyzed by the established 3D-QSAR model. Finally, 8 compounds with desired properties were retained from the final hits and have been assigned to another research group to complete the follow-up compound synthesis and biologic evaluation.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China. .,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
| | - Chen Shen
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Hong-Rui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Wen-Xuan Chen
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Qing-Qing Luo
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
| | - Lan Ding
- College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China
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18
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LEI B, ZANG Y, XUE Z, GE Y, LI W, ZHAI Q, JIAO L. [Ensemble hologram quantitative structure activity relationship model of the chromatographic retention index of aldehydes and ketones]. Se Pu 2021; 39:331-337. [PMID: 34227314 PMCID: PMC9403813 DOI: 10.3724/sp.j.1123.2020.06011] [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: 06/04/2020] [Indexed: 11/25/2022] Open
Abstract
Chromatographic retention index (RI) is an important parameter for describing the retention behavior of substances in chromatographic analysis. Experimentally determining the RI values of different aldehyde and ketone compounds in all kinds of polar stationary phases is expensive and time consuming. Quantitative structure activity relationship (QSAR) is an important chemometric technique that has been widely used to correlate the properties of chemicals to their molecular structures. Irrespective of whether the properties of a molecule have been experimentally determined, they can be calculated using QSAR models. It is therefore necessary and advisable to establish the QSAR model for predicting the RI value of aldehydes and ketones. Hologram QSAR (HQSAR) is a highly efficient QSAR approach that can easily generate QSAR models with good statistics and high prediction accuracy. A specific fragment of fingerprint, known as a molecular hologram, is proposed in the HQSAR approach and used as a structural descriptor to build the proposed QSAR model. In general, individual HQSAR models are built in QSAR researches. However, individual QSAR models are usually affected by underfitting and overfitting. The ensemble modeling method, which integrate several individual models through certain consensus strategies, can overcome the shortcomings of individual models. It is worth studying whether ensemble modeling can improve the prediction ability of the HQSAR method in order to build more accurate and reliable QSAR models. Therefore, this study investigates the QSAR model for chromatographic RI of aldehydes and ketones using ensemble modeling and the HQSAR method. Two individual HQSAR models comprising 34 compounds in two stationary phases, DB-210 and HP-Innowax, were established. The prediction ability of the two established models was assessed by external test set validation and leave-one-out cross validation (LOO-CV). The investigated 34 compounds were randomly assigned into two groups. Group Ⅰ comprised 26 compounds, and Group Ⅱ comprised 8 compounds. In the validation of the external test set, Group Ⅰ was employed to manually optimize the two fragment parameters (fragment distinction (FD) and fragment size (FS)) and build the HQSAR models. Group Ⅱ was used as the test set to assess the predictive performance of the developed models. For the DB-210 stationary phase, the optimal individual HQSAR model was obtained while setting the FD and FS to "donor/acceptor atoms (DA)" and 1-9, respectively. For the HP-Innowax stationary phase, the optimal individual HQSAR model was obtained by setting the FD and FS to "DA" and 4-7 respectively. The squared correlation coefficient of cross validation ( [Formula: see text] for predicting the RI values of the DB-210 and HP-Innowax stationary phases were 0.927 and 0.919, 0.956 and 0.979, 0.929 and 0.963, 0.927 and 0.958, and 0.935 and 0.963, respectively. Compared to the individual HQSAR models, the established ensemble HQSAR models show better robustness and accuracy, thus establishing that ensemble modeling is an effective approach. The combination of HQSAR and the ensemble modeling method is a practicable and promising method for studying and predicting the RI values of aldehydes and ketones.
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19
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López AFF, Martínez OMM, Hernández HFC. Evaluation of Amaryllidaceae alkaloids as inhibitors of human acetylcholinesterase by QSAR analysis and molecular docking. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2020.129142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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20
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Chen ZD, Zhao L, Chen HY, Gong JN, Chen X, Chen CYC. A novel artificial intelligence protocol to investigate potential leads for Parkinson's disease. RSC Adv 2020; 10:22939-22958. [PMID: 35520357 PMCID: PMC9054719 DOI: 10.1039/d0ra04028b] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 05/27/2020] [Indexed: 11/21/2022] Open
Abstract
Previous studies have shown that small molecule inhibitors of NLRP3 may be a potential treatment for Parkinson's disease (PD). NACHT, LRR and PYD domains-containing protein 3 (NLRP3), heat shock protein HSP 90-beta (HSP90AB1), caspase-1 (CASP1) and cellular tumor antigen p53 (TP53) have significant involvement in the pathogenesis pathway of PD. Molecular docking was used to screen the traditional Chinese medicine database TCM Database@Taiwan. Top traditional Chinese medicine (TCM) compounds with high affinities based on Dock Score were selected to form the drug-target interaction network to investigate potential candidates targeting NLRP3, HSP90AB1, CASP1, and TP53 proteins. Artificial intelligence model, 3D-Quantitative Structure-Activity Relationship (3D-QSAR) were constructed respectively utilizing training sets of inhibitors against the four proteins with known inhibitory activities (pIC50). The results showed that 2007_22057 (an indole derivative), 2007_22325 (a valine anhydride) and 2007_15317 (an indole derivative) might be a potential medicine formula for the treatment of PD. Then there are three candidate compounds identified by the result of molecular dynamics.
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Affiliation(s)
- Zhi-Dong Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
- School of Pharmaceutical Sciences, Sun Yat-sen University Shenzhen 510275 China
| | - Lu Zhao
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
| | - Hsin-Yi Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
| | - Jia-Ning Gong
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
- School of Pharmaceutical Sciences, Sun Yat-sen University Shenzhen 510275 China
| | - Xu Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
- School of Pharmaceutical Sciences, Sun Yat-sen University Shenzhen 510275 China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
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21
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Jiao L, Liu H, Qu L, Xue Z, Wang Y, Wang Y, Lei B, Zang Y, Xu R, Zhang Z, Li H, Alyemeni OAA. QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index. ACS OMEGA 2020; 5:3878-3888. [PMID: 32149214 PMCID: PMC7057327 DOI: 10.1021/acsomega.9b03139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 02/07/2020] [Indexed: 05/10/2023]
Abstract
The quantitative structure-property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n-heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.
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Affiliation(s)
- Long Jiao
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
- E-mail:
| | - Huanhuan Liu
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Le Qu
- State
Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chendu University of Technology, Chendu 610059, P. R. China
- Shaanxi
Cooperative Innovation Center of Unconventional Oil and Gas Exploration
and Development, Xi’an Shiyou University, Xi’an 710065, P. R. China
| | - Zhiwei Xue
- No.
203 Research Institute of Nuclear Industry, Xianyang 712000, P. R. China
| | - Yuan Wang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Yanzhao Wang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Bin Lei
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Yunlei Zang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Rui Xu
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Zhen Zhang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Hua Li
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
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22
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Chen X, Chen HY, Chen ZD, Gong JN, Chen CYC. A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia. J Mater Chem B 2020; 8:2063-2081. [PMID: 32068215 DOI: 10.1039/d0tb00061b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug development, and it plays a key role in drug discovery. Based on network pharmacology and virtual screening, candidates were selected from the molecular database. Nine artificial intelligence algorithm models were used to further verify the candidates' potential. The 350 training results of the deep learning model showed higher credibility, and the R-square of the training set and test set of the optimal model reached 0.89 and 0.84, respectively. The random forest model has an R-square of 0.91 and a mean square error of only 0.003. The R-square of the Adaptive Boosting model and the Bagging model reached 0.92 and 0.88, respectively. Molecular dynamics simulation evaluated the stability of the ligand-protein complex and achieved good results. Artificial intelligence models had unearthed the promising candidates for STAT3 inhibitors, and the good performance of most models showed that they still had practical value on small data sets.
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Affiliation(s)
- Xu Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China. and School of Pharmaceutical Sciences, Sun Yat-sen University, Shenzhen, 510275, China
| | - Hsin-Yi Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China.
| | - Zhi-Dong Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China. and School of Pharmaceutical Sciences, Sun Yat-sen University, Shenzhen, 510275, China
| | - Jia-Ning Gong
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China. and School of Pharmaceutical Sciences, Sun Yat-sen University, Shenzhen, 510275, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China. and Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan and Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Hologram QSAR study on the critical micelle concentration of Gemini surfactants. Colloids Surf A Physicochem Eng Asp 2020. [DOI: 10.1016/j.colsurfa.2019.124226] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Zhang Y, Han Z, Gao Q, Bai X, Zhang C, Hou H. Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches. Curr Pharm Des 2019; 25:4296-4302. [PMID: 31696803 DOI: 10.2174/1381612825666191107092214] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/04/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. METHODS In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. RESULTS The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. CONCLUSION This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.
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Affiliation(s)
- Yuan Zhang
- Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Zhenyan Han
- Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Qian Gao
- Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Xiaoyi Bai
- Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, China.,University of Auckland, Auckland, New Zealand
| | - Hongying Hou
- Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
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