Literature DB >> 28274840

Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.

Maykel Cruz-Monteagudo1, Stephan Schürer2, Eduardo Tejera3, Yunierkis Pérez-Castillo4, José L Medina-Franco5, Aminael Sánchez-Rodríguez6, Fernanda Borges7.   

Abstract

Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.
Copyright © 2017 Elsevier Ltd. All rights reserved.

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Year:  2017        PMID: 28274840      PMCID: PMC5487293          DOI: 10.1016/j.drudis.2017.02.004

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  92 in total

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Authors:  Albert-László Barabási; Zoltán N Oltvai
Journal:  Nat Rev Genet       Date:  2004-02       Impact factor: 53.242

2.  On outliers and activity cliffs--why QSAR often disappoints.

Authors:  Gerald M Maggiora
Journal:  J Chem Inf Model       Date:  2006 Jul-Aug       Impact factor: 4.956

Review 3.  Molecular networks as sensors and drivers of common human diseases.

Authors:  Eric E Schadt
Journal:  Nature       Date:  2009-09-10       Impact factor: 49.962

Review 4.  The interplay between molecular modeling and chemoinformatics to characterize protein-ligand and protein-protein interactions landscapes for drug discovery.

Authors:  José L Medina-Franco; Oscar Méndez-Lucio; Karina Martinez-Mayorga
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5.  Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts.

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Journal:  Future Med Chem       Date:  2014       Impact factor: 3.808

6.  GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

Authors:  Yunierkis Pérez-Castillo; Cosmin Lazar; Jonatan Taminau; Mathy Froeyen; Miguel Ángel Cabrera-Pérez; Ann Nowé
Journal:  J Chem Inf Model       Date:  2012-08-28       Impact factor: 4.956

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Authors:  B Millauer; S Wizigmann-Voos; H Schnürch; R Martinez; N P Møller; W Risau; A Ullrich
Journal:  Cell       Date:  1993-03-26       Impact factor: 41.582

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Authors:  Daniel J Rigden; Xosé M Fernández-Suárez; Michael Y Galperin
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  8 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.  Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling.

Authors:  Tianyi Qiu; Dingfeng Wu; Jingxuan Qiu; Zhiwei Cao
Journal:  J Cheminform       Date:  2018-04-12       Impact factor: 5.514

3.  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

4.  A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing.

Authors:  Alejandro Cabrera-Andrade; Andrés López-Cortés; Gabriela Jaramillo-Koupermann; Humberto González-Díaz; Alejandro Pazos; Cristian R Munteanu; Yunierkis Pérez-Castillo; Eduardo Tejera
Journal:  Pharmaceuticals (Basel)       Date:  2020-11-22

Review 5.  G protein-coupled receptors: structure- and function-based drug discovery.

Authors:  Dehua Yang; Qingtong Zhou; Viktorija Labroska; Shanshan Qin; Sanaz Darbalaei; Yiran Wu; Elita Yuliantie; Linshan Xie; Houchao Tao; Jianjun Cheng; Qing Liu; Suwen Zhao; Wenqing Shui; Yi Jiang; Ming-Wei Wang
Journal:  Signal Transduct Target Ther       Date:  2021-01-08

6.  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.

Authors:  Rhoda Oyeladun Oyewole; Abel Kolawole Oyebamiji; Banjo Semire
Journal:  Heliyon       Date:  2020-05-21

7.  Chemoproteomic identification of molecular targets of antifungal prototypes, thiosemicarbazide and a camphene derivative of thiosemicarbazide, in Paracoccidioides brasiliensis.

Authors:  Joyce Villa Verde Bastos Borba; Sinji Borges Ferreira Tauhata; Cecília Maria Alves de Oliveira; Monique Ferreira Marques; Alexandre Melo Bailão; Célia Maria de Almeida Soares; Maristela Pereira
Journal:  PLoS One       Date:  2018-08-27       Impact factor: 3.240

8.  Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

Authors:  E Tejera; I Carrera; Karina Jimenes-Vargas; V Armijos-Jaramillo; A Sánchez-Rodríguez; M Cruz-Monteagudo; Y Perez-Castillo
Journal:  PLoS One       Date:  2019-10-07       Impact factor: 3.240

  8 in total

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