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Han D, Lu J, Fan B, Lu W, Xue Y, Wang M, Liu T, Cui S, Gao Q, Duan Y, Xu Y. Lysine-Specific Demethylase 1 Inhibitors: A Comprehensive Review Utilizing Computer-Aided Drug Design Technologies. Molecules 2024; 29:550. [PMID: 38276629 PMCID: PMC10821146 DOI: 10.3390/molecules29020550] [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: 11/29/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/27/2024] Open
Abstract
Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.
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Affiliation(s)
- Di Han
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Jiarui Lu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Baoyi Fan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Wenfeng Lu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Yiwei Xue
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Meiting Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Taigang Liu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
| | - Shaoli Cui
- School of Forensic, Xinxiang Medical University, Xinxiang 453003, China
| | - Qinghe Gao
- School of Pharmacy, Xinxiang Medical University, Xinxiang 453003, China
| | - Yingchao Duan
- School of Pharmacy, Xinxiang Medical University, Xinxiang 453003, China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China; (D.H.); (J.L.)
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang 453003, China
- Xinxiang Key Laboratory of Biomedical Information Research, Xinxiang 453003, China
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Darsaraee M, Kaveh S, Mani-Varnosfaderani A, Neiband MS. General structure-activity/selectivity relationship patterns for the inhibitors of the chemokine receptors (CCR1/CCR2/CCR4/CCR5) with application for virtual screening of PubChem database. J Biomol Struct Dyn 2023:1-19. [PMID: 37599469 DOI: 10.1080/07391102.2023.2248255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/08/2023] [Indexed: 08/22/2023]
Abstract
CC chemokine receptors (CCRs) form a crucial subfamily of G protein-linked receptors that play a distinct role in the onset and progression of various life-threatening diseases. The main aim of this research is to derive general structure-activity relationship (SAR) patterns to describe the selectivity and activity of CCR inhibitors. To this end, a total of 7332 molecules related to the inhibition of CCR1, CCR2, CCR4, and CCR5 were collected from the Binding Database and analyzed using machine learning techniques. A diverse set of 450 molecular descriptors was calculated for each molecule, and the molecules were classified based on their therapeutic targets and activities. The variable importance in the projection (VIP) approach was used to select discriminatory molecular features, and classification models were developed using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN). The reliability and predictability of the models were estimated using 10-fold cross-validation, an external validation set, and an applicability domain approach. We were able to identify different sets of molecular descriptors for discriminating between active and inactive molecules and model the selectivity of inhibitors towards different CCRs. The sensitivities of the predictions for the external test set for the SKN models ranged from 0.827-0.873. Finally, the developed classification models were used to screen approximately 2 million random molecules from the PubChem database, with average values for areas under the receiver operating characteristic curves ranging from 0.78-0.96 for SKN models and 0.75-0.89 for CPANN models.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- M Darsaraee
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - S Kaveh
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - A Mani-Varnosfaderani
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - M S Neiband
- Department of Chemistry, Payame Noor University (PNU), Tehran, Iran
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Rezaie H, Asadollahi-Baboli M, Hassaninejad-Darzi SK. Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:779-792. [PMID: 36330747 DOI: 10.1080/1062936x.2022.2139292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.
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Affiliation(s)
- H Rezaie
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol, Iran
| | - M Asadollahi-Baboli
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol, Iran
| | - S K Hassaninejad-Darzi
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol, Iran
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Zhou J, Wu S, Lee BG, Chen T, He Z, Lei Y, Tang B, Hirst JD. Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1. Molecules 2021; 26:7492. [PMID: 34946572 PMCID: PMC8707381 DOI: 10.3390/molecules26247492] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/05/2021] [Accepted: 12/06/2021] [Indexed: 12/01/2022] Open
Abstract
A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.
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Affiliation(s)
- Jiajun Zhou
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Shiying Wu
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Boon Giin Lee
- School of Computer Science, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China;
| | - Tianwei Chen
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Ziqi He
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Yukun Lei
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Bencan Tang
- Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; (J.Z.); (S.W.); (T.C.); (Z.H.); (Y.L.)
| | - Jonathan D. Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK
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Kalaki Z, Asadollahi-Baboli M. Molecular docking-based classification and systematic QSAR analysis of indoles as Pim kinase inhibitors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:399-419. [PMID: 32319325 DOI: 10.1080/1062936x.2020.1751277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
Pim kinase enzyme has an essential role in the treatment of prostate, colon and acute myeloid leukaemia cancers. The indoles inhibitors were docked in the enzyme's active pocket in order to survey the inhibition mechanism and extract the ligands' conformations. The docking outcome shows that the active inhibitors have strong van der Waals interactions with residues of Ile185, Leu44, Leu120 and Leu174, hydrogen bonds with residues of Asp128, Arg122 and Glu171 and π-π interaction with the residue of Phe49. The sum of these interactions is ~80 kcal mol-1 contributing ~90% of total binding free energies. Using docking-based molecular descriptors, the unsupervised and supervised classifications were successfully carried out with the accuracy of 0.82 and 0.95, respectively, to categorize the active/inactive Pim kinase inhibitors. The vigorous quantitative assessment was performed using different machine learning techniques. The constructed QSAR model [(r 2 cal, r 2 p, r 2 m and Q 2 LOO) > 0.80 and (SE cal, SEp and SE LOO) < 0.22] indicates that the molecular descriptors of nN, RDF20v and E1v can describe both the inhibition activities and the inhibition mechanism. The adequate evaluations of the molecular docking, classifications and QSAR analysis show that the current approaches can be used as valuable tools to design more effective new Pim kinase inhibitors for cancer treatment.
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Affiliation(s)
- Z Kalaki
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology , Babol, Iran
| | - M Asadollahi-Baboli
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology , Babol, Iran
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