Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features-Reference-Cited by-同舟云学术

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Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features

Published:2022-12-02 Issue:12 Volume:17 Page:1425-1441
ISSN:1746-0441
Container-title:Expert Opinion on Drug Discovery
language:en
Short-container-title:Expert Opinion on Drug Discovery

Author:

Ciray Fulya¹²,Doğan Tunca¹³⁴^ORCID

Affiliation:

1. Biological Data Science Laboratory, Department of Computer Engineering, Hacettepe University, Ankara, Turkey

2. Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey

3. Department of Health Informatics, Institute of Informatics, Hacettepe University, Ankara, Turkey

4. Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey

Funder

TUBITAK BIDEB-2211-A PhD Fellowship Program

Publisher

Informa UK Limited

Subject

Drug Discovery

Link

https://www.tandfonline.com/doi/pdf/10.1080/17460441.2023.2153830

Reference46 articles.

1. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

2. Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

3. CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

4. DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations

5. MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery

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