Literature DB >> 18452123

Desirability-based multiobjective optimization for global QSAR studies: application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles.

Maykel Cruz-Monteagudo1, Fernanda Borges, M Natália D S Cordeiro.   

Abstract

Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylphenyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability-based MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs. 2008 Wiley Periodicals, Inc.

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Year:  2008        PMID: 18452123     DOI: 10.1002/jcc.20994

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  7 in total

Review 1.  From flamingo dance to (desirable) drug discovery: a nature-inspired approach.

Authors:  Aminael Sánchez-Rodríguez; Yunierkis Pérez-Castillo; Stephan C Schürer; Orazio Nicolotti; Giuseppe Felice Mangiatordi; Fernanda Borges; M Natalia D S Cordeiro; Eduardo Tejera; José L Medina-Franco; Maykel Cruz-Monteagudo
Journal:  Drug Discov Today       Date:  2017-06-15       Impact factor: 7.851

2.  Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies.

Authors:  Prabu Manoharan; R S K Vijayan; Nanda Ghoshal
Journal:  J Comput Aided Mol Des       Date:  2010-08-26       Impact factor: 3.686

Review 3.  On exploring structure-activity relationships.

Authors:  Rajarshi Guha
Journal:  Methods Mol Biol       Date:  2013

4.  A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents.

Authors:  Yunierkis Perez-Castillo; Aminael Sánchez-Rodríguez; Eduardo Tejera; Maykel Cruz-Monteagudo; Fernanda Borges; M Natália D S Cordeiro; Huong Le-Thi-Thu; Hai Pham-The
Journal:  PLoS One       Date:  2018-02-08       Impact factor: 3.240

5.  Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept".

Authors:  Fisayo A Olotu; Geraldene Munsamy; Mahmoud E S Soliman
Journal:  Comput Struct Biotechnol J       Date:  2018-11-23       Impact factor: 7.271

6.  Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Authors:  Lun K Tsou; Shiu-Hwa Yeh; Shau-Hua Ueng; Chun-Ping Chang; Jen-Shin Song; Mine-Hsine Wu; Hsiao-Fu Chang; Sheng-Ren Chen; Chuan Shih; Chiung-Tong Chen; Yi-Yu Ke
Journal:  Sci Rep       Date:  2020-10-08       Impact factor: 4.379

Review 7.  Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

Authors:  Varnavas D Mouchlis; Antreas Afantitis; Angela Serra; Michele Fratello; Anastasios G Papadiamantis; Vassilis Aidinis; Iseult Lynch; Dario Greco; Georgia Melagraki
Journal:  Int J Mol Sci       Date:  2021-02-07       Impact factor: 5.923

  7 in total

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