1
|
El Fadili M, Er-Rajy M, Mujwar S, Ajala A, Bouzammit R, Kara M, Abuelizz HA, Er-Rahmani S, Elhallaoui M. In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations. BMC Chem 2024; 18:142. [PMID: 39085870 PMCID: PMC11293250 DOI: 10.1186/s13065-024-01248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885 and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation with correlation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation technique with the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability domain (AD), in addition to Fisher's and Student's statistical tests. Thereafter, ten novel molecules were designed based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined for their similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen for molecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex with the targeted protein of NMDA receptor (5EWJ.pdb).
Collapse
Affiliation(s)
- Mohamed El Fadili
- LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco.
| | - Mohammed Er-Rajy
- LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco
| | - Somdutt Mujwar
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, 140401, India
| | - Abduljelil Ajala
- Department of chemistry, Faculty of physical sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Rachid Bouzammit
- Engineering Laboratory of Organometallic, Molecular Materials and Environment (LIMOME), Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco
| | - Mohammed Kara
- Laboratory of Biotechnology, Conservation and Valorization of Naturals Resources, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco
| | - Hatem A Abuelizz
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Sara Er-Rahmani
- Dipartimento di Chimica, Università di Torino, Torino, 10125, Italy
| | - Menana Elhallaoui
- LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, 30000, Morocco
| |
Collapse
|
2
|
Zhao Y, Wan Q, He X. Construction of IRAK4 inhibitor activity prediction model based on machine learning. Mol Divers 2024:10.1007/s11030-024-10926-5. [PMID: 38970641 DOI: 10.1007/s11030-024-10926-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024]
Abstract
Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R2 of 0.829, an MAE of 0.317, and an RMSE of 0.460 in independent testing. To further validate the model's generalization ability, we tested it on 90 IRAK4 inhibitors collected in 2023. Subsequently, we applied the model to predict the activity of 13,268 compounds with docking scores less than - 9.503 kcal/mol. These compounds were initially screened from a pool of 1.6 million molecules in the chemdiv database through high-throughput molecular docking. Among these, 259 compounds with predicted pIC50 values greater than or equal to 8.00 were identified. We then performed ADMET predictions on these selected compounds. Finally, through a rigorous screening process, we identified 34 compounds that adhere to the four complementary drug-likeness rules, making them promising candidates for further investigation. Additionally, molecular dynamics simulations confirmed the stable binding of the screened compounds to the IRAK4 protein. Overall, this work presents a machine learning model for accurate prediction of IRAK4 inhibitor activity and offers new insights for subsequent structure-guided design of novel IRAK4 inhibitors.
Collapse
Affiliation(s)
- Yihuan Zhao
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China.
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China.
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China.
| | - Qianwen Wan
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China
| | - Xiaoyu He
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China
| |
Collapse
|
3
|
Janicka M, Sztanke M, Sztanke K. Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology. Molecules 2024; 29:287. [PMID: 38257200 PMCID: PMC11154582 DOI: 10.3390/molecules29020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.
Collapse
Affiliation(s)
- Małgorzata Janicka
- Department of Physical Chemistry, Faculty of Chemistry, Institute of Chemical Science, Maria Curie-Skłodowska University, 20-031 Lublin, Poland;
| | - Małgorzata Sztanke
- Department of Medical Chemistry, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland;
| | - Krzysztof Sztanke
- Laboratory of Bioorganic Compounds Synthesis and Analysis, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland
| |
Collapse
|
4
|
El fadili M, Er-rajy M, Imtara H, Noman OM, Mothana RA, Abdullah S, Zerougui S, Elhallaoui M. QSAR, ADME-Tox, molecular docking and molecular dynamics simulations of novel selective glycine transporter type 1 inhibitors with memory enhancing properties. Heliyon 2023; 9:e13706. [PMID: 36865465 PMCID: PMC9971180 DOI: 10.1016/j.heliyon.2023.e13706] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
A structural class of forty glycine transporter type 1 (GlyT1) inhibitors, was examined using molecular modeling techniques. The quantitative structure-activity relationships (QSAR) technology confirmed that human GlyT1 activity is strongly and significantly affected by constitutional, geometrical, physicochemical and topological descriptors. ADME-Tox in-silico pharmacokinetics revealed that L28 and L30 ligands were predicted as non-toxic inhibitors with a good ADME profile and the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicated that the predicted inhibitors block GlyT1, reacting specifically with Phe319, Phe325, Tyr123, Tyr 124, Arg52, Asp475, Ala117, Ala479, Ile116 and Ile483 amino acids of the dopamine transporter (DAT) membrane protein. These results were qualified and strengthened using molecular dynamics (MD) study, which affirmed that the established intermolecular interactions for (L28, L30-DAT protein) complexes remain perfectly stable along 50 ns of MD simulation time. Therefore, they could be strongly recommended as therapeutics in medicine to improve memory performance.
Collapse
Affiliation(s)
- Mohamed El fadili
- LIMAS Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, BP 1796 Atlas, Fez 30000, Morocco,Corresponding author.
| | - Mohammed Er-rajy
- LIMAS Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, BP 1796 Atlas, Fez 30000, Morocco
| | - Hamada Imtara
- Faculty of Arts and Sciences, Arab American University Palestine, Jenin BP Box 240, Palestine
| | - Omar M. Noman
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Ramzi A. Mothana
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Sheaf Abdullah
- Department of Hand Surgery and Microsurgery, University Medicine Greifswald, Greifswald, Germany
| | - Sara Zerougui
- LIMAS Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, BP 1796 Atlas, Fez 30000, Morocco
| | - Menana Elhallaoui
- LIMAS Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, BP 1796 Atlas, Fez 30000, Morocco
| |
Collapse
|
5
|
Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds. Int J Mol Sci 2022; 23:ijms232415887. [PMID: 36555527 PMCID: PMC9786067 DOI: 10.3390/ijms232415887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022] Open
Abstract
The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.
Collapse
|
6
|
Zhu T, Tao C, Cheng H, Cong H. Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 846:157455. [PMID: 35863580 DOI: 10.1016/j.scitotenv.2022.157455] [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: 05/25/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haibing Cong
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| |
Collapse
|
7
|
Structural Model Based on Genetic Algorithm for Inhibiting Fatty Acid Amide Hydrolase. AI 2022. [DOI: 10.3390/ai3040052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The fatty acid amide hydrolase (FAAH) is an enzyme responsible for the degradation of anandamide, an endocannabinoid. Pharmacologically blocking this target can lead to anxiolytic effects; therefore, new inhibitors can improve therapy in this field. In order to speed up the process of drug discovery, various in silico methods can be used, such as molecular docking, quantitative structure–activity relationship models (QSAR), and artificial intelligence (AI) classification algorithms. Besides architecture, one important factor for an AI model with high accuracy is the dataset quality. This issue can be solved by a genetic algorithm that can select optimal features for the prediction. The objective of the current study is to use this feature selection method in order to identify the most relevant molecular descriptors that can be used as independent variables, thus improving the efficacy of AI algorithms that can predict FAAH inhibitors. The model that used features chosen by the genetic algorithm had better accuracy than the model that used all molecular descriptors generated by the CDK descriptor calculator 1.4.6 software. Hence, carefully selecting the input data used by AI classification algorithms by using a GA is a promising strategy in drug development.
Collapse
|
8
|
El fadili M, Er-Rajy M, Kara M, Assouguem A, Belhassan A, Alotaibi A, Mrabti NN, Fidan H, Ullah R, Ercisli S, Zarougui S, Elhallaoui M. QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia. Pharmaceuticals (Basel) 2022; 15:ph15060670. [PMID: 35745588 PMCID: PMC9228289 DOI: 10.3390/ph15060670] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 02/04/2023] Open
Abstract
Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated with physicochemical, geometrical and topological descriptors, in particular: Hydrogen bond donor, polarizability, surface tension, stretch and torsion energies and topological diameter. According to in silico ADMET properties, the most active ligands (L6, L9, L30, L31 and L37) are the molecules having the highest probability of penetrating the central nervous system (CNS), but the molecule 32 has the highest probability of being absorbed by the gastrointestinal tract. Molecular docking results indicate that Tyr124, Phe43, Phe325, Asp46, Phe319 and Val120 amino acids are the active sites of the dopamine transporter (DAT) membrane protein, in which the most active ligands can inhibit the glycine transporter type 1 (GlyT1). The results of molecular dynamics (MD) simulation revealed that all five inhibitors remained stable in the active sites of the DAT protein during 100 ns, demonstrating their promising role as candidate drugs for the treatment of schizophrenia.
Collapse
Affiliation(s)
- Mohamed El fadili
- Engineering Materials, Modeling and Environmental Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco; (M.E.-R.); (N.N.M.); (S.Z.); (M.E.)
- Correspondence: (M.E.f.); (M.K.)
| | - Mohammed Er-Rajy
- Engineering Materials, Modeling and Environmental Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco; (M.E.-R.); (N.N.M.); (S.Z.); (M.E.)
| | - Mohammed Kara
- Laboratory of Biotechnology, Conservation and Valorisation of Naturals Resources, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
- Correspondence: (M.E.f.); (M.K.)
| | - Amine Assouguem
- Laboratory of Functional Ecology and Environment, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Imouzzer Street, Fez 30000, Morocco;
| | - Assia Belhassan
- Molecular Chemistry and Natural Substances Laboratory, Department of Chemistry, Faculty of Sciences, University Moulay Ismail, Meknes 50000, Morocco;
| | - Amal Alotaibi
- Department of Basic Science, College of Medicine, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Nidal Naceiri Mrabti
- Engineering Materials, Modeling and Environmental Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco; (M.E.-R.); (N.N.M.); (S.Z.); (M.E.)
| | - Hafize Fidan
- Department of Tourism and Culinary Management, Faculty of Economics, University of Food Technologies, 4000 Plovdiv, Bulgaria;
| | - Riaz Ullah
- Department of Pharmacognosy (MAPPRC), College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Sezai Ercisli
- Department of Horticulture, Agricultural Faculty, Ataturk University, Erzurum TR-25240, Turkey;
| | - Sara Zarougui
- Engineering Materials, Modeling and Environmental Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco; (M.E.-R.); (N.N.M.); (S.Z.); (M.E.)
| | - Menana Elhallaoui
- Engineering Materials, Modeling and Environmental Laboratory, Faculty of Sciences Dhar El Mehraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco; (M.E.-R.); (N.N.M.); (S.Z.); (M.E.)
| |
Collapse
|
9
|
Zhu T, Chen W, Gu Y, Jafvert CT, Fu D. Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation. WATER RESEARCH 2021; 207:117799. [PMID: 34731669 DOI: 10.1016/j.watres.2021.117799] [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: 06/27/2021] [Revised: 10/01/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (KPE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large KPE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of KPE-w. These models exhibit good goodness-of-fit (R2adj: 0.919-0.975), robustness (Q2LOO: 0.870-0.954) and external prediction performances (Q2ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for KPE-w. This result also heightens interest in measuring and predicting the KPE-w values of chemicals containing halogen atoms in water.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
| | - Yuanyuan Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
| |
Collapse
|
10
|
Zhu T, Gu L, Chen M, Sun F. Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. CHEMOSPHERE 2021; 266:128962. [PMID: 33218721 DOI: 10.1016/j.chemosphere.2020.128962] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 06/11/2023]
Abstract
Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (KPUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the KPUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Liming Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Feng Sun
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| |
Collapse
|
11
|
Yuyama M, Ito T, Arai Y, Kadowaki Y, Iiyama N, Keino A, Hiraoka Y, Kanaya T, Momose Y, Kurihara M. Risk Prediction Method for Anticholinergic Action Using Auto-quantitative Structure-Activity Relationship and Docking Study with Molecular Operating Environment. Chem Pharm Bull (Tokyo) 2021; 68:773-778. [PMID: 32741919 DOI: 10.1248/cpb.c20-00249] [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: 11/22/2022]
Abstract
Lower urinary tract symptoms (LUTS) induced by anticholinergic drug action impair the QOL of patients and are associated with a poor prognosis. Therefore, it is expedient to develop methods of predicting the anticholinergic side effects of drugs, which we aimed to achieve in this study using a quantitative structure-activity relationship (QSAR) and docking study with molecular operations environment (MOE; Molecular Simulation Informatics Systems [MOLSIS], Inc.) In the QSAR simulation, the QSAR model built using the partial least squares regression (PLS) and genetic algorithm-multiple linear regression (GA-MLR) methods showed remarkable coefficient of determination (R2) and XR2 values. In the docking study, a specific relationship was identified between the adjusted docking score (-S) and bioactivity (pKi) values. In conclusion, the methods developed could be useful for in silico risk assessment of LUTS, and plans are potentially applicable to numerous drugs with anticholinergic activity that induce serious side effects, limiting their use.
Collapse
Affiliation(s)
- Materu Yuyama
- Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
| | - Takeshi Ito
- Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School.,Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Yumiko Arai
- Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
| | - Yuki Kadowaki
- Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Natsumi Iiyama
- Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Ayako Keino
- Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Yurina Hiraoka
- Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Takayuki Kanaya
- Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Yasuyuki Momose
- Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School.,Department of Pharmaceutical Sciences, International University of Health and Welfare
| | - Masaaki Kurihara
- Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School.,Department of Pharmaceutical Sciences, International University of Health and Welfare
| |
Collapse
|
12
|
Vahedi N, Mohammadhosseini M, Nekoei M. QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches. CURR ANAL CHEM 2020. [DOI: 10.2174/1573411016999200518083359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily
present in eukaryotes.
Methods:
In the present report, some efficient linear and non-linear methods including multiple linear
regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully
used to develop and establish quantitative structure-activity relationship (QSAR) models
capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP
inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set
and selection of the training and test sets. A genetic algorithm (GA) variable selection method was
employed to select the optimal subset of descriptors that have the most significant contributions to
the overall inhibitory activity from the large pool of calculated descriptors.
Results:
The accuracy and predictability of the proposed models were further confirmed using crossvalidation,
validation through an external test set and Y-randomization (chance correlations) approaches.
Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed
models. The results revealed that non-linear modeling approaches, including SVM and ANN
could provide much more prediction capabilities.
Conclusion:
Among the constructed models and in terms of root mean square error of predictions
(RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for
the training set, the predictive power of the GA-SVM approach was better. However, compared with
MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.
Collapse
Affiliation(s)
- Nafiseh Vahedi
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Mehdi Nekoei
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| |
Collapse
|
13
|
Zhu T, Gu Y, Cheng H, Chen M. Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 728:138881. [PMID: 32361362 DOI: 10.1016/j.scitotenv.2020.138881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/19/2020] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (KPOM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log KPOM-w with R2adj of 0.825. The values of statistical parameters including R2adj, Q2ext, RMSEtra and RMSEext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yuanyuan Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing 210096, China; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| |
Collapse
|
14
|
Oyewole RO, Oyebamiji AK, Semire B. Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches. Heliyon 2020; 6:e03926. [PMID: 32462084 PMCID: PMC7243141 DOI: 10.1016/j.heliyon.2020.e03926] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 03/26/2020] [Accepted: 05/01/2020] [Indexed: 02/08/2023] Open
Abstract
This work used quantum chemical method via DFT to calculate molecular descriptors for the development of QSAR model to predict bioactivity (IC50- 50% inhibition concentration) of the selected 1, 2, 3-triazole-pyrimidine derivatives against receptor (human gastric cancer cell line, MGC-803). The selected molecular parameters were obtained by B3LYP/6-31G∗∗. QSAR model linked the molecular parameters of the studied compounds to their cytotoxicity and reproduced their observed bioactivities against MGC-803. The calculated IC50 tailored the observed IC50 and greater than standard compound, 5-fluorouracil, suggesting that the developed QSAR model reproduced the observed bioactivity. Statistical analyses (including R2, CV. R2 andR a 2 gave 0.950, 0.970 and 0.844 respectively) revealed a very good fitness. Molecular docking studies revealed the hydrogen bonding with the amino acid residues in the binding site, as well as ligand conformations which are essential feature for ligand-receptor interactions. Therefore, the methods used in this study are veritable tools that can be employed in pharmacological and medicinal chemistry researches in designing better drugs with improve potency.
Collapse
Affiliation(s)
- Rhoda Oyeladun Oyewole
- Department of Pure and Applied Chemistry, Faculty of Pure and Applied Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Abel Kolawole Oyebamiji
- Department of Pure and Applied Chemistry, Faculty of Pure and Applied Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
- Department of Basic Sciences, Adeleke University, P.M.B. 250, Ede, Osun State, Nigeria
| | - Banjo Semire
- Department of Pure and Applied Chemistry, Faculty of Pure and Applied Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| |
Collapse
|
15
|
Hamadache M, Benkortbi O, Hanini S, Amrane A. QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:896-907. [PMID: 29067614 DOI: 10.1007/s11356-017-0498-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/16/2017] [Indexed: 06/07/2023]
Abstract
Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q 2 and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.
Collapse
Affiliation(s)
- Mabrouk Hamadache
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria.
| | - Othmane Benkortbi
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Salah Hanini
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Abdeltif Amrane
- Ecole Nationale Supérieure de Chimie de Rennes, CNRS, UMR 6226, Université de Rennes 1, 11 allée de Beaulieu, 35708, Rennes Cedex 7, CS 50837, France
| |
Collapse
|
16
|
Golmohammadi H, Dashtbozorgi Z, Khooshechin S. Modeling and predicting the solute polarity parameter in reversed-phase liquid chromatography using quantitative structure-property relationship approaches. J Sep Sci 2017; 40:4495-4502. [DOI: 10.1002/jssc.201700603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/13/2017] [Accepted: 09/14/2017] [Indexed: 11/07/2022]
Affiliation(s)
- Hassan Golmohammadi
- Young Researchers and Elite Club, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch; Islamic Azad University; Tehran Iran
| | - Zahra Dashtbozorgi
- Young Researchers and Elite Club, Central Tehran Branch; Islamic Azad University; Tehran Iran
| | - Sajad Khooshechin
- Young Researchers and Elite Club, Central Tehran Branch; Islamic Azad University; Tehran Iran
| |
Collapse
|
17
|
Bellera CL, Di Ianni ME, Talevi A. The application of molecular topology for ulcerative colitis drug discovery. Expert Opin Drug Discov 2017; 13:89-101. [PMID: 29088918 DOI: 10.1080/17460441.2018.1396314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.
Collapse
Affiliation(s)
- Carolina L Bellera
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Mauricio E Di Ianni
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Alan Talevi
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| |
Collapse
|
18
|
Karadžić MŽ, Jevrić LR, Mandić AI, Markov SL, Podunavac-Kuzmanović SO, Kovačević SZ, Nikolić AR, Oklješa AM, Sakač MN, Penov-Gaši KM. Chemometrics approach based on chromatographic behavior, in silico characterization and molecular docking study of steroid analogs with biomedical importance. Eur J Pharm Sci 2017; 105:71-81. [DOI: 10.1016/j.ejps.2017.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 04/23/2017] [Accepted: 05/03/2017] [Indexed: 01/05/2023]
|
19
|
Synthesis, biological screening, POM, and 3D-QSAR analyses of some novel pyrazolic compounds. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1888-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
20
|
Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
Collapse
Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
| |
Collapse
|
21
|
Golmohammadi H, Dashtbozorgi Z. QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:977-997. [PMID: 27658742 DOI: 10.1080/1062936x.2016.1233138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Accepted: 09/02/2016] [Indexed: 06/06/2023]
Abstract
In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r2, between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.
Collapse
Affiliation(s)
- H Golmohammadi
- a Young Researchers and Elite Club , Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University , Tehran , Iran
| | - Z Dashtbozorgi
- b Young Researchers and Elite Club, Central Tehran Branch , Islamic Azad University , Tehran , Iran
| |
Collapse
|
22
|
Beheshti A, Pourbasheer E, Nekoei M, Vahdani S. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. JOURNAL OF SAUDI CHEMICAL SOCIETY 2016. [DOI: 10.1016/j.jscs.2012.07.019] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
23
|
Aalizadeh R, Pourbasheer E, Ganjali MR. Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies. Mol Divers 2015; 19:915-30. [DOI: 10.1007/s11030-015-9626-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2015] [Accepted: 07/27/2015] [Indexed: 12/14/2022]
|
24
|
Pourbasheer E, Aalizadeh R, Ardabili JS, Ganjali MR. QSPR study on solubility of some fullerenes derivatives using the genetic algorithms — Multiple linear regression. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.01.028] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
25
|
QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach. Med Chem Res 2015. [DOI: 10.1007/s00044-015-1354-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
26
|
Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
Collapse
Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| |
Collapse
|
27
|
|
28
|
QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods. Med Chem Res 2012. [DOI: 10.1007/s00044-012-0412-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|