What are the current challenges for machine learning in drug discovery and repurposing?-Reference-Cited by-同舟云学术

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What are the current challenges for machine learning in drug discovery and repurposing?

Published:2022-03-08 Issue:5 Volume:17 Page:423-425
ISSN:1746-0441
Container-title:Expert Opinion on Drug Discovery
language:en
Short-container-title:Expert Opinion on Drug Discovery

Author:

Aittokallio Tero¹²³^ORCID

Affiliation:

1. Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland

2. Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway

3. Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway

Publisher

Informa UK Limited

Subject

Drug Discovery

Link

https://www.tandfonline.com/doi/pdf/10.1080/17460441.2022.2050694

Reference16 articles.

1. Applications of machine learning in drug discovery and development

2. Artificial Intelligence in Cancer Research and Precision Medicine

3. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

4. A critical overview of computational approaches employed for COVID-19 drug discovery

5. DOME: recommendations for supervised machine learning validation in biology

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1. Drug recommendation ranking for personalized medicine using outcomes of retrospective cancer patients;Expert Systems with Applications;2024-12

2. Prediction and principle discovery of drug combination based on multimodal friendship features;2024-08-30

3. Editorial overview–Artificial intelligence methodologies in structural biology: Bridging the gap to medical applications;Current Opinion in Structural Biology;2024-08

4. Computational biology approaches for drug repurposing;Progress in Molecular Biology and Translational Science;2024

5. Integrating multi-modal deep learning on knowledge graph for the discovery of synergistic drug combinations against infectious diseases;Cell Reports Physical Science;2023-08

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