Literature DB >> 31083984

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models.

Pravin Ambure1, Amit Kumar Halder1, Humbert González Díaz2, M Natália D S Cordeiro1.   

Abstract

Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Entities:  

Year:  2019        PMID: 31083984     DOI: 10.1021/acs.jcim.9b00295

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  9 in total

1.  In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

Authors:  Maria Chiara Astuto; Matteo R Di Nicola; José V Tarazona; A Rortais; Yann Devos; A K Djien Liem; George E N Kass; Maria Bastaki; Reinhilde Schoonjans; Angelo Maggiore; Sandrine Charles; Aude Ratier; Christelle Lopes; Ophelia Gestin; Tobin Robinson; Antony Williams; Nynke Kramer; Edoardo Carnesecchi; Jean-Lou C M Dorne
Journal:  Methods Mol Biol       Date:  2022

2.  Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors.

Authors:  Saw Simeon; Nathjanan Jongkon
Journal:  Molecules       Date:  2019-12-01       Impact factor: 4.411

3.  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

4.  DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.

Authors:  Yash Khemchandani; Stephen O'Hagan; Soumitra Samanta; Neil Swainston; Timothy J Roberts; Danushka Bollegala; Douglas B Kell
Journal:  J Cheminform       Date:  2020-09-04       Impact factor: 5.514

5.  QSAR-Co-X: an open source toolkit for multitarget QSAR modelling.

Authors:  Amit Kumar Halder; M Natália Dias Soeiro Cordeiro
Journal:  J Cheminform       Date:  2021-04-15       Impact factor: 5.514

6.  Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases.

Authors:  Amit Kumar Halder; M Natália D S Cordeiro
Journal:  Biomolecules       Date:  2021-11-10

Review 7.  Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Authors:  Amit Kumar Halder; Ana S Moura; Maria Natália D S Cordeiro
Journal:  Int J Mol Sci       Date:  2022-04-29       Impact factor: 5.923

8.  Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.

Authors:  Amit Kumar Halder; Amal Kanta Giri; Maria Natália Dias Soeiro Cordeiro
Journal:  Molecules       Date:  2019-10-30       Impact factor: 4.411

9.  A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures.

Authors:  Kota Kurosaki; Raymond Wu; Yoshihiro Uesawa
Journal:  Int J Mol Sci       Date:  2020-10-23       Impact factor: 5.923

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.