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Banat R, Daoud S, Taha MO. Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes. Mol Divers 2024:10.1007/s11030-024-10814-y. [PMID: 38446372 DOI: 10.1007/s11030-024-10814-y] [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: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 03/07/2024]
Abstract
Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.
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Affiliation(s)
- Rajaa Banat
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan
| | - Safa Daoud
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Sciences Private University, Amman, Jordan
| | - Mutasem Omar Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
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Zerroug E, Belaidi S, Chtita S, Tuffaha G, AbulQais F, Kciuk M, Dubey A, Taha MO. Structure‐Based Approaches for the Prediction of Alzheimer's Disease Inhibitors: Comparative Interactions Analysis, Pharmacophore Modeling and Molecular Dynamics Simulations. ChemistrySelect 2024; 9. [DOI: 10.1002/slct.202303307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 12/18/2023] [Indexed: 07/10/2024]
Abstract
AbstractDue to its significant role in neurodegeneration, Cyclin‐dependent kinase 5 (CDK5) has emerged as a potential target for addressing neuropathological disorders, including Alzheimer's disease (AD). The application of CDK5 inhibitors has demonstrated promise in the treatment of AD. This prompted us to model this interesting target using a computational workflow named Docking‐based Comparative Intermolecular Contacts Analysis (dbCICA). Approaches including 3D‐QSAR, genetic algorithm, and pharmacophore modeling were employed to discover new CDK inhibitors.
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Affiliation(s)
- Enfale Zerroug
- Group of Computational and Pharmaceutical Chemistry LMCE Laboratory University of Biskra, BP 145 Biskra 07000 Algeria
| | - Salah Belaidi
- Group of Computational and Pharmaceutical Chemistry LMCE Laboratory University of Biskra, BP 145 Biskra 07000 Algeria
| | - Samir Chtita
- Faculty of Sciences Ben M'Sik Hassan II University of Casablanca Sidi Othman, Casablanca Morocco
| | | | - Faizan AbulQais
- Department of Agricultural Microbiology Aligarh Muslim University Aligarh UP 202002 India
| | - Mateusz Kciuk
- Doctoral School of Exact and Natural Sciences University of Lodz Banacha Street 12/16 90-237 Lodz Poland
- Department of Molecular Biotechnology and Genetics University of Lodz Banacha 12/16 90-237 Lodz Poland
| | - Amit Dubey
- Department of Pharmacology Saveetha Dental College and Hospital Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India
| | - Mutasem O. Taha
- Drug Discovery Unit Department of Pharmaceutical Sciences Faculty of Pharmacy University of Jordan 11942 Amman Jordan
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Daoud S, Alabed SJ, Bardaweel SK, Taha MO. Discovery of potent maternal embryonic leucine zipper kinase (MELK) inhibitors of novel chemotypes using structure-based pharmacophores. Med Chem Res 2023; 32:2574-2586. [DOI: 10.1007/s00044-023-03160-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/05/2023] [Indexed: 07/10/2024]
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Al-Sha'er MA, Basheer HA, Taha MO. Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores. Mol Divers 2023; 27:443-462. [PMID: 35507210 DOI: 10.1007/s11030-022-10434-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/05/2022] [Indexed: 12/13/2022]
Abstract
Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC50 values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.
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Affiliation(s)
- Mahmoud A Al-Sha'er
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan.
| | - Haneen A Basheer
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
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Alabed SJ, Zihlif M, Taha M. Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning. RSC Adv 2022; 12:35873-35895. [PMID: 36545090 PMCID: PMC9751883 DOI: 10.1039/d2ra05102h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.
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Affiliation(s)
- Shada J Alabed
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan Amman Jordan
| | - Malek Zihlif
- Department of Pharmacology, Faculty of Medicine, University of Jordan Amman Jordan
| | - Mutasem Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan Amman Jordan
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“MedChemVR”: A Virtual Reality Game to Enhance Medicinal Chemistry Education. MULTIMODAL TECHNOLOGIES AND INTERACTION 2021. [DOI: 10.3390/mti5030010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Medicinal chemistry (MC) is an indispensable component of the pharmacy curriculum. The pharmacists’ unique knowledge of a medicine’s chemistry enhances their understanding of the pharmacological activity, manufacturing, storage, use, supply, and handling of drugs. However, chemistry is a challenging subject for both teaching and learning. These challenges are typically caused by the inability of students to construct a mental image of the three-dimensional (3D) structure of a drug molecule from its two-dimensional presentations. This study explores a prototype virtual reality (VR) gamification option, as an educational tool developed to aid the learning process and to improve the delivery of the MC subject to students. The developed system is evaluated by a cohort of 41 students. The analysis of the results was encouraging and provided invaluable feedback for the future development of the proposed system.
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Hijjawi MS, Abutayeh RF, Taha MO. Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA). Molecules 2020; 25:molecules25246003. [PMID: 33353031 PMCID: PMC7766225 DOI: 10.3390/molecules25246003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/12/2023] Open
Abstract
Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied—aiming to identify novel Aurora-A kinase inhibitors—using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC50 values of 3.5–11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC50 on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.
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Affiliation(s)
- Majd S Hijjawi
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman 11942, Jordan
| | - Reem Fawaz Abutayeh
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science Private University, Amman 11931, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman 11942, Jordan
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Daoud S, Taha MO. Pharmacophore modeling of JAK1: A target infested with activity-cliffs. J Mol Graph Model 2020; 99:107615. [PMID: 32339898 DOI: 10.1016/j.jmgm.2020.107615] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/01/2020] [Accepted: 04/02/2020] [Indexed: 12/14/2022]
Abstract
Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC50 of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.
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Affiliation(s)
- Safa Daoud
- Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science Private University, Amman, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
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Abutayeh RF, Taha MO. Discovery of novel Flt3 inhibitory chemotypes through extensive ligand-based and new structure-based pharmacophore modelling methods. J Mol Graph Model 2019; 88:128-151. [PMID: 30703688 DOI: 10.1016/j.jmgm.2019.01.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 01/03/2019] [Accepted: 01/17/2019] [Indexed: 01/10/2023]
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Al-Sha'er MA, Al-Aqtash RA, Taha MO. Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis. Med Chem 2019; 15:588-601. [PMID: 30799792 DOI: 10.2174/1573406415666190222125333] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/31/2019] [Accepted: 02/07/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. METHODS Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). RESULTS The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. CONCLUSION Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.
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Affiliation(s)
- Mahmoud A Al-Sha'er
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Rua'a A Al-Aqtash
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan
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Hatmal MM, Taha MO. Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand-Receptor Complexes. J Chem Inf Model 2018. [PMID: 29529367 DOI: 10.1021/acs.jcim.7b00708] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar IC50s.
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Affiliation(s)
- Ma'mon M Hatmal
- Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences , The Hashemite University , P.O. Box 330127 , Zarqa 13133 , Jordan
| | - Mutasem O Taha
- Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy , University of Jordan , Amman 11942 , Jordan
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Al-Sha'er MA, Taha MO. Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor. J Mol Graph Model 2018; 83:153-166. [PMID: 29456101 DOI: 10.1016/j.jmgm.2018.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 01/01/2018] [Accepted: 02/02/2018] [Indexed: 11/26/2022]
Abstract
Akt1 and Akt3 are important serine/threonine-specific protein kinases involved in G2 phase required by cancer cells to maintain cell cycle and to prevent cell death. Accordingly, inhibitors of these kinases should have potent anti-cancer properties. This prompted us to use pharmacophore/QSAR modeling to identify optimal binding models and physicochemical descriptors that explain bioactivity variation within a set of 74 diverse Akt3 inhibitors. Two successful orthogonal pharmacophores were identified and further validated using receiver operating characteristic (ROC) curve analyses. The pharmacophoric models and associated QSAR equation were applied to screen the national cancer institute (NCI) list of compounds for new Akt3 inhibitors. Six hits showed significant experimental anti-Akt3 IC50 values, out of which one compound exhibited dual low micromolar anti-Akt1 and anti-Akt3 inhibitory profiles.
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Affiliation(s)
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan.
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Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling. Future Med Chem 2017; 9:1141-1159. [PMID: 28722471 DOI: 10.4155/fmc-2017-0061] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
AIM Ligand-based pharmacophore modeling requires long list of inhibitors, while pharmacophores based on single ligand-receptor crystallographic structure can be too restricted or promiscuous. METHODOLOGY This prompted us to combine simulated annealing molecular dynamics (SAMD) with ligand-receptor contacts analysis as means to construct pharmacophore model(s) from single ligand-receptor complex. Ligand-receptor contacts that survive numerous heating-cooling SAMD cycles are considered critical and are used to guide pharmacophore development. RESULTS This methodology was implemented to develop pharmacophores for acetylcholinesterase and protein kinase C-θ. The resulting models were validated by receiver-operating characteristic analysis and in vitro bioassay. Assay identified four new protein kinase C-θ inhibitors among captured hits, two of which exhibited nanomolar potencies. CONCLUSION The results illustrate the ability of the new method to extract valid pharmacophores from single ligand-protein complex.
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Docking-based comparative intermolecular contacts analysis and in silico screening reveal new potent acetylcholinesterase inhibitors. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1976-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation. Future Med Chem 2016; 8:1841-1869. [PMID: 27643626 DOI: 10.4155/fmc-2016-0056] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
AIM FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC50 of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy. CONCLUSION Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.
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