Improvement of multi-task learning by data enrichment: application for drug discovery-Reference-Cited by-同舟云学术

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Improvement of multi-task learning by data enrichment: application for drug discovery

Published:2023-03-21 Issue:4 Volume:37 Page:183-200
ISSN:0920-654X
Container-title:Journal of Computer-Aided Molecular Design
language:en
Short-container-title:J Comput Aided Mol Des

Author:

Sosnina Ekaterina A.,Sosnin Sergey,Fedorov Maxim V.

Funder

Russian Foundation for Basic Research

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/s10822-023-00500-w.pdf

Reference47 articles.

1. Williams AJ, Pence HE (2017) The future of chemical information is now. Chem Int 39(3):9–14. https://doi.org/10.1515/ci-2017-0304

2. Tetko IV, Engkvist O, Chen H (2016) Does ‘Big Data’ exist in medicinal chemistry, and if so, how can it be harnessed? Future Med Chem 8(15):1801–1806. https://doi.org/10.4155/fmc-2016-0163

3. Nikitina AA, Orlov AA, Kozlovskaya LI, Palyulin VA, Osolodkin DI (2019) Enhanced taxonomy annotation of antiviral activity data from ChEMBL. Database 2019:139. https://doi.org/10.1093/database/bay139

4. Sosnin S, Karlov D, Tetko IV, Fedorov MV (2019) Comparative study of multitask toxicity modeling on a broad chemical space. J Chem Inf Model 59(3):1062–1072. https://doi.org/10.1021/acs.jcim.8b00685

5. Jain S, Siramshetty VB, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Nicklaus MC, Simeonov A, Zakharov AV (2021) Large-scale modeling of multispecies acute toxicity end points using consensus of multitask deep learning methods. J Chem Inf Model 61(2):653–663. https://doi.org/10.1021/acs.jcim.0c01164

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