Literature DB >> 29445894

Sparse QSAR modelling methods for therapeutic and regenerative medicine.

David A Winkler1,2,3,4,5.   

Abstract

The quantitative structure-activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.

Keywords:  Deep learning; Machine learning; QSAR; Quantitative structure–activity relationships; Regenerative medicine; Skolnik award; Sparse feature selection

Mesh:

Substances:

Year:  2018        PMID: 29445894     DOI: 10.1007/s10822-018-0106-1

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  26 in total

1.  The broader applications of neural and genetic modelling methods.

Authors:  Dave Winkler
Journal:  Drug Discov Today       Date:  2001-12-01       Impact factor: 7.851

2.  Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitors.

Authors:  David A Winkler; Frank R Burden
Journal:  Mol Biosyst       Date:  2012-01-26

Review 3.  Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials.

Authors:  David A Winkler
Journal:  Toxicol Appl Pharmacol       Date:  2015-12-23       Impact factor: 4.219

4.  Optimization of drug combinations using Feedback System Control.

Authors:  Patrycja Nowak-Sliwinska; Andrea Weiss; Xianting Ding; Paul J Dyson; Hubert van den Bergh; Arjan W Griffioen; Chih-Ming Ho
Journal:  Nat Protoc       Date:  2016-01-14       Impact factor: 13.491

5.  Toward novel universal descriptors: charge fingerprints.

Authors:  Frank R Burden; Mitchell J Polley; David A Winkler
Journal:  J Chem Inf Model       Date:  2009-03       Impact factor: 4.956

6.  Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.

Authors:  D L J Alexander; A Tropsha; David A Winkler
Journal:  J Chem Inf Model       Date:  2015-07-09       Impact factor: 4.956

7.  Relevance Vector Machines: Sparse Classification Methods for QSAR.

Authors:  Frank R Burden; David A Winkler
Journal:  J Chem Inf Model       Date:  2015-07-21       Impact factor: 4.956

8.  Modeling biological activities of nanoparticles.

Authors:  V Chandana Epa; Frank R Burden; Carlos Tassa; Ralph Weissleder; Stanley Shaw; David A Winkler
Journal:  Nano Lett       Date:  2012-10-09       Impact factor: 11.189

9.  Change correlations in structure-activity studies using multiple regression analysis.

Authors:  J G Topliss; R J Costello
Journal:  J Med Chem       Date:  1972-10       Impact factor: 7.446

10.  Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage.

Authors:  Aaron W Thornton; Cory M Simon; Jihan Kim; Ohmin Kwon; Kathryn S Deeg; Kristina Konstas; Steven J Pas; Matthew R Hill; David A Winkler; Maciej Haranczyk; Berend Smit
Journal:  Chem Mater       Date:  2017-03-08       Impact factor: 9.811

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  1 in total

Review 1.  Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.

Authors:  David A Winkler
Journal:  Front Chem       Date:  2021-03-15       Impact factor: 5.221

  1 in total

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