MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling
Author:
Funder
CAPES
CNPq
FAPESP
FAPEMIG
Pró-Reitoria de Pesquisa of the Universidade Federal de Minas Gerais
Publisher
Springer Science and Business Media LLC
Subject
Physical and Theoretical Chemistry,Computer Science Applications,Drug Discovery
Link
https://link.springer.com/content/pdf/10.1007/s10822-023-00536-y.pdf
Reference61 articles.
1. Yang X, Wang Y, Byrne R et al (2019) Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev 119:10520–10594. https://doi.org/10.1021/acs.chemrev.8b00728
2. Masand VH, Mahajan DT, Nazeruddin GM et al (2015) Effect of information leakage and method of splitting (rational and random) on external predictive ability and behavior of different statistical parameters of QSAR model. Med Chem Res 24:1241–1264. https://doi.org/10.1007/s00044-014-1193-8
3. Andrada MF, Vega-Hissi EG, Estrada MR, Garro Martinez JC (2017) Impact assessment of the rational selection of training and test sets on the predictive ability of QSAR models. SAR QSAR Environ Res 28:1011–1023. https://doi.org/10.1080/1062936X.2017.1397056
4. Clark DE (2006) What has computer-aided molecular design ever done for drug discovery? Expert Opin Drug Discov 1:103–110. https://doi.org/10.1517/17460441.1.2.103
5. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (2017) Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk
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