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QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads
ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the g...
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Published in: | Molecules (Basel, Switzerland) Switzerland), 2022-08, Vol.27 (15), p.4951 |
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creator | Jawarkar, Rahul D Sharma, Praveen Jain, Neetesh Gandhi, Ajaykumar Mukerjee, Nobendu Al-Mutairi, Aamal A Zaki, Magdi E A Al-Hussain, Sami A Samad, Abdul Masand, Vijay H Ghosh, Arabinda Bakal, Ravindra L |
description | ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor. |
doi_str_mv | 10.3390/molecules27154951 |
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In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.</description><identifier>ISSN: 1420-3049</identifier><identifier>EISSN: 1420-3049</identifier><identifier>DOI: 10.3390/molecules27154951</identifier><identifier>PMID: 35956900</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>ALK tyrosine kinase inhibitors ; Anaplastic Lymphoma Kinase ; anticancer ; Antitumor activity ; Biological activity ; Chemical bonds ; Chemists ; Datasets ; Drug development ; Drug resistance ; Enzyme inhibitors ; Fluorine ; Gene amplification ; Genetic algorithms ; Kinases ; Lipophilic ; Lung cancer ; Lymphoma ; Mathematical models ; MD simulation ; MMGBSA ; Molecular docking ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Nitrogen ; Optimization ; Protein Kinase Inhibitors - chemistry ; Protein Kinase Inhibitors - pharmacology ; Protein-tyrosine kinase ; Proteins ; QSAR ; Quantitative Structure-Activity Relationship ; Signal transduction ; Simulation ; Solvents ; Statistical analysis ; Structure-activity relationships ; Tyrosine</subject><ispartof>Molecules (Basel, Switzerland), 2022-08, Vol.27 (15), p.4951</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.</description><subject>ALK tyrosine kinase inhibitors</subject><subject>Anaplastic Lymphoma Kinase</subject><subject>anticancer</subject><subject>Antitumor activity</subject><subject>Biological activity</subject><subject>Chemical bonds</subject><subject>Chemists</subject><subject>Datasets</subject><subject>Drug development</subject><subject>Drug resistance</subject><subject>Enzyme inhibitors</subject><subject>Fluorine</subject><subject>Gene amplification</subject><subject>Genetic algorithms</subject><subject>Kinases</subject><subject>Lipophilic</subject><subject>Lung cancer</subject><subject>Lymphoma</subject><subject>Mathematical models</subject><subject>MD simulation</subject><subject>MMGBSA</subject><subject>Molecular docking</subject><subject>Molecular Docking Simulation</subject><subject>Molecular Dynamics Simulation</subject><subject>Nitrogen</subject><subject>Optimization</subject><subject>Protein Kinase Inhibitors - chemistry</subject><subject>Protein Kinase Inhibitors - pharmacology</subject><subject>Protein-tyrosine kinase</subject><subject>Proteins</subject><subject>QSAR</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Signal transduction</subject><subject>Simulation</subject><subject>Solvents</subject><subject>Statistical analysis</subject><subject>Structure-activity relationships</subject><subject>Tyrosine</subject><issn>1420-3049</issn><issn>1420-3049</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNplUk1vEzEQXSEQLYUfwAVZ4sKBgL82G1-Q0pSWqIkKSTmvJvYk67Brb-1dpPY_8p9wSKhaONl68-a95_Fk2WtGPwih6MfG16j7GiMvWC5Vzp5kx0xyOhBUqqcP7kfZixi3lHImWf48OxK5yoeK0uPs17flePGezPdKEMiZ1z-s2yTojCxtk7DOekfAGTKfX5wux2QCtT7AkYzbNnjQFUbSebJA7TfO3iGZeKcRajTkawWhAe3bygeryTlC14dEX-BNb6PtkKx9IF2F5KrtbGPv9oZ-TcazS3J9G3y0DsmldRCRTF1lV7bzIRJI7q6zGpJTIDMEE19mz9ZQR3x1OE-y7-efrydfBrOri-lkPBtoqUQ3GGkBOYiRHBqhJVubnFMlDKcrpTnyoVpLarTkoPNRISWAKIaFUciMKZikVJxk072u8bAt22AbCLelB1v-AXzYlBBStBrLkUHJi3xlkILMDQNasJXIkVHGtVAiaX3aa7X9qkGj0XUB6keijyvOVuXG_yyVKKgUuzDvDgLB3_QYu7KxUWNdg0Pfx5IX6d9HNFc8Ud_-Q936Prg0qh2LFlwOh0VisT1Lp9nHgOv7MIyWu8Ur_1u81PPm4SvuO_5umvgNtLbY2g</recordid><startdate>20220803</startdate><enddate>20220803</enddate><creator>Jawarkar, Rahul D</creator><creator>Sharma, Praveen</creator><creator>Jain, Neetesh</creator><creator>Gandhi, Ajaykumar</creator><creator>Mukerjee, Nobendu</creator><creator>Al-Mutairi, Aamal A</creator><creator>Zaki, Magdi E A</creator><creator>Al-Hussain, Sami A</creator><creator>Samad, Abdul</creator><creator>Masand, Vijay H</creator><creator>Ghosh, Arabinda</creator><creator>Bakal, Ravindra L</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2125-6596</orcidid><orcidid>https://orcid.org/0000-0003-3563-6642</orcidid><orcidid>https://orcid.org/0000-0002-7129-7003</orcidid><orcidid>https://orcid.org/0000-0001-6518-6131</orcidid><orcidid>https://orcid.org/0000-0002-6628-7927</orcidid><orcidid>https://orcid.org/0000-0002-3891-5949</orcidid></search><sort><creationdate>20220803</creationdate><title>QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads</title><author>Jawarkar, Rahul D ; Sharma, Praveen ; Jain, Neetesh ; Gandhi, Ajaykumar ; Mukerjee, Nobendu ; Al-Mutairi, Aamal A ; Zaki, Magdi E A ; Al-Hussain, Sami A ; Samad, Abdul ; Masand, Vijay H ; Ghosh, Arabinda ; Bakal, Ravindra L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c493t-8c3a5a3846d3c41fd52093d20b9c2e269f40dc42ac58744aa3767d9e1dd714003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>ALK tyrosine kinase inhibitors</topic><topic>Anaplastic Lymphoma Kinase</topic><topic>anticancer</topic><topic>Antitumor activity</topic><topic>Biological activity</topic><topic>Chemical bonds</topic><topic>Chemists</topic><topic>Datasets</topic><topic>Drug development</topic><topic>Drug resistance</topic><topic>Enzyme inhibitors</topic><topic>Fluorine</topic><topic>Gene amplification</topic><topic>Genetic algorithms</topic><topic>Kinases</topic><topic>Lipophilic</topic><topic>Lung cancer</topic><topic>Lymphoma</topic><topic>Mathematical models</topic><topic>MD simulation</topic><topic>MMGBSA</topic><topic>Molecular docking</topic><topic>Molecular Docking Simulation</topic><topic>Molecular Dynamics Simulation</topic><topic>Nitrogen</topic><topic>Optimization</topic><topic>Protein Kinase Inhibitors - 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In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35956900</pmid><doi>10.3390/molecules27154951</doi><orcidid>https://orcid.org/0000-0002-2125-6596</orcidid><orcidid>https://orcid.org/0000-0003-3563-6642</orcidid><orcidid>https://orcid.org/0000-0002-7129-7003</orcidid><orcidid>https://orcid.org/0000-0001-6518-6131</orcidid><orcidid>https://orcid.org/0000-0002-6628-7927</orcidid><orcidid>https://orcid.org/0000-0002-3891-5949</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | ALK tyrosine kinase inhibitors Anaplastic Lymphoma Kinase anticancer Antitumor activity Biological activity Chemical bonds Chemists Datasets Drug development Drug resistance Enzyme inhibitors Fluorine Gene amplification Genetic algorithms Kinases Lipophilic Lung cancer Lymphoma Mathematical models MD simulation MMGBSA Molecular docking Molecular Docking Simulation Molecular Dynamics Simulation Nitrogen Optimization Protein Kinase Inhibitors - chemistry Protein Kinase Inhibitors - pharmacology Protein-tyrosine kinase Proteins QSAR Quantitative Structure-Activity Relationship Signal transduction Simulation Solvents Statistical analysis Structure-activity relationships Tyrosine |
title | QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T07%3A42%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=QSAR,%20Molecular%20Docking,%20MD%20Simulation%20and%20MMGBSA%20Calculations%20Approaches%20to%20Recognize%20Concealed%20Pharmacophoric%20Features%20Requisite%20for%20the%20Optimization%20of%20ALK%20Tyrosine%20Kinase%20Inhibitors%20as%20Anticancer%20Leads&rft.jtitle=Molecules%20(Basel,%20Switzerland)&rft.au=Jawarkar,%20Rahul%20D&rft.date=2022-08-03&rft.volume=27&rft.issue=15&rft.spage=4951&rft.pages=4951-&rft.issn=1420-3049&rft.eissn=1420-3049&rft_id=info:doi/10.3390/molecules27154951&rft_dat=%3Cproquest_doaj_%3E2700724667%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c493t-8c3a5a3846d3c41fd52093d20b9c2e269f40dc42ac58744aa3767d9e1dd714003%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2700724667&rft_id=info:pmid/35956900&rfr_iscdi=true |