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Sardar S, Jyotisha, Amin SA, Khatun S, Qureshi IA, Patil UK, Jha T, Gayen S. Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics. J Biomol Struct Dyn 2024; 42:5642-5656. [PMID: 38870352 DOI: 10.1080/07391102.2023.2227710] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 06/15/2023] [Indexed: 06/15/2024]
Abstract
Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sourav Sardar
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Jyotisha
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Sk Abdul Amin
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Samima Khatun
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Insaf Ahmed Qureshi
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Umesh Kumar Patil
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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2
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Dey PK, Dutta R, Ray M, Jakkula P, Banerjee S, Qureshi IA, Gayen S, Amin SA. Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:483-504. [PMID: 38904353 DOI: 10.1080/1062936x.2024.2366886] [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: 04/02/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.
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Affiliation(s)
- P K Dey
- Department of Pharmaceutical Technology, JIS University, Kolkata, West Bengal, India
| | - R Dutta
- Department of Pharmaceutical Technology, JIS University, Kolkata, West Bengal, India
| | - M Ray
- Department of Pharmaceutical Technology, JIS University, Kolkata, West Bengal, India
| | - P Jakkula
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - S Banerjee
- Department of Pharmaceutical Technology, JIS University, Kolkata, West Bengal, India
| | - I A Qureshi
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - S Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S A Amin
- Department of Pharmaceutical Technology, JIS University, Kolkata, West Bengal, India
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Dudas B, Miteva MA. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 2024; 45:39-55. [PMID: 38072723 DOI: 10.1016/j.tips.2023.11.001] [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: 08/02/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.
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Affiliation(s)
- Balint Dudas
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France
| | - Maria A Miteva
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France.
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Tajiani F, Ahmadi S, Lotfi S, Kumar P, Almasirad A. In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization. BMC Chem 2023; 17:87. [PMID: 37496005 PMCID: PMC10373329 DOI: 10.1186/s13065-023-00999-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model ([Formula: see text]= 0.727, [Formula: see text]= 0.628, [Formula: see text]= 0.642, and [Formula: see text]=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC50 were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC50 of eight natural flavonols with pIC50 more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).
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Affiliation(s)
- Faezeh Tajiani
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, 19395-4697, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Updated chemical scaffolds of ABCG2 inhibitors and their structure-inhibition relationships for future development. Eur J Med Chem 2022; 241:114628. [DOI: 10.1016/j.ejmech.2022.114628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/07/2022] [Accepted: 07/21/2022] [Indexed: 11/19/2022]
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De P, Kar S, Ambure P, Roy K. Prediction reliability of QSAR models: an overview of various validation tools. Arch Toxicol 2022; 96:1279-1295. [PMID: 35267067 DOI: 10.1007/s00204-022-03252-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/14/2022] [Indexed: 01/20/2023]
Abstract
The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | | | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Das S, Amin SA, Gayen S, Jha T. Insight into the structural requirements of gelatinases (MMP-2 and MMP-9) inhibitors by multiple validated molecular modelling approaches: Part II. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:167-192. [PMID: 35301933 DOI: 10.1080/1062936x.2022.2041722] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
Inhibition of the matrix metalloproteinases (MMPs) is effective against metastasis of secondary tumours. Previous MMP inhibitors have failed in clinical trials due to their off-target toxicity in solid tumours. Thus, newer MMP inhibitors now have paramount importance. Here, different molecular modelling techniques were applied on a dataset of 110 gelatinase (MMP-2 and MMP-9) inhibitors. The objectives of the present study were to identify structural fingerprints for gelatinase inhibition and also to develop statistically validated QSAR models for the screening and prediction of different derivatives as MMP-2 (gelatinase A) and MMP-9 (gelatinase B) inhibitors. The Bayesian classification study provided the ROC values for the training set of 0.837 and 0.815 for MMP-2 and MMP-9, respectively. The linear model also produced the leave-one-out cross-validated Q2 of 0.805 (eq. 1, MMP-2) and 0.724 (eq. 2, MMP-9), an r2 of 0.845 (eq. 1, MMP-2) and 0.782 (eq. 2, MMP-9), an r2Pred of 0.806 (eq. 1, MMP-2) and 0.732 (eq. 2, MMP-9). Similarly, non-linear learning models were also statistically significant and reliable. Overall, this study may help in the rational design of newer compounds with higher gelatinase inhibition to fight against both primary and secondary cancers in future.
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Affiliation(s)
- S Das
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S A Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - T Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Ahmadi S, Lotfi S, Afshari S, Kumar P, Ghasemi E. CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:1013-1031. [PMID: 34875951 DOI: 10.1080/1062936x.2021.2003429] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Global QSAR modelling was performed to predict the pIC50 values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF1 (WIIC = 0) and TF2 (WIIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF2 are more reliable and robust and were used to predict the pIC50 values. The model constructed for split 4 via TF2 is regarded as the best model and the numerical values of r2Train, r2Valid, Q2Train and Q2Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC50 of BTK inhibitors were also identified.
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Affiliation(s)
- S Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - S Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, Iran
| | - S Afshari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - E Ghasemi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Nandi S, Kumar P, Amin SA, Jha T, Gayen S. First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:917-939. [PMID: 34727793 DOI: 10.1080/1062936x.2021.1989721] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 10/03/2021] [Indexed: 06/13/2023]
Abstract
Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.
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Affiliation(s)
- S Nandi
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India
| | - P Kumar
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
| | - S A Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - T Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Gayen
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [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: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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