A semi-supervised learning framework for quantitative structure–activity regression modelling | Zendy

Oliver P Watson | Zendy; Isidro CortésCiriano | Zendy; James A Watson | Zendy

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A semi-supervised learning framework for quantitative structure–activity regression modelling

Author(s) -

Oliver P Watson,

Isidro CortésCiriano,

James A Watson

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa711

Subject(s) - computer science , regression , regression analysis , artificial intelligence , machine learning , supervised learning , statistics , mathematics , artificial neural network

Quantitative structure-activity relationship (QSAR) methods are increasingly used in assisting the process of preclinical, small molecule drug discovery. Regression models are trained on data consisting of a finite-dimensional representation of molecular structures and their corresponding target-specific activities. These supervised learning models can then be used to predict the activity of previously unmeasured novel compounds.

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