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Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery
- Source :
- Journal of Cheminformatics, Vol 11, Iss 1, Pp 1-16 (2019), Journal of Cheminformatics
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Structure–activity relationship modelling is frequently used in the early stage of drug discovery to assess the activity of a compound on one or several targets, and can also be used to assess the interaction of compounds with liability targets. QSAR models have been used for these and related applications over many years, with good success. Conformal prediction is a relatively new QSAR approach that provides information on the certainty of a prediction, and so helps in decision-making. However, it is not always clear how best to make use of this additional information. In this article, we describe a case study that directly compares conformal prediction with traditional QSAR methods for large-scale predictions of target-ligand binding. The ChEMBL database was used to extract a data set comprising data from 550 human protein targets with different bioactivity profiles. For each target, a QSAR model and a conformal predictor were trained and their results compared. The models were then evaluated on new data published since the original models were built to simulate a “real world” application. The comparative study highlights the similarities between the two techniques but also some differences that it is important to bear in mind when the methods are used in practical drug discovery applications. Electronic supplementary material The online version of this article (10.1186/s13321-018-0325-4) contains supplementary material, which is available to authorized users.
- Subjects :
- Quantitative structure–activity relationship
Computer science
ChEMBL
Conformal map
Library and Information Sciences
Machine learning
computer.software_genre
01 natural sciences
Mondrian conformal prediction
lcsh:Chemistry
03 medical and health sciences
Classification models
Prediction methods
Physical and Theoretical Chemistry
030304 developmental biology
0303 health sciences
lcsh:T58.5-58.64
QSAR
lcsh:Information technology
Drug discovery
business.industry
Cheminformatics
Scale (chemistry)
chEMBL
Computer Graphics and Computer-Aided Design
0104 chemical sciences
Computer Science Applications
Data set
010404 medicinal & biomolecular chemistry
lcsh:QD1-999
Artificial intelligence
business
computer
Research Article
Subjects
Details
- ISSN :
- 17582946
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Journal of Cheminformatics
- Accession number :
- edsair.doi.dedup.....28b076bd70e57f179f84b216a612018a