Deep learning approaches in predicting ADMET properties-Reference-Cited by-同舟云学术

Deep learning approaches in predicting ADMET properties

Published:2020-11 Issue:22 Volume:12 Page:1995-1999
ISSN:1756-8919
Container-title:Future Medicinal Chemistry
language:en
Short-container-title:Future Medicinal Chemistry

Author:

Cáceres Elena L¹²^ORCID,Tudor Matthew³,Cheng Alan C¹^ORCID

Affiliation:

1. Computational & Structural Chemistry, Merck & Co., Inc., South San Francisco, CA 94080, USA

2. Department of Pharmaceutical Chemistry; Graduate Program in Biological & Medical Informatics; Department of Bioengineering & Therapeutic Sciences; Bakar Computational Health Sciences Institute; Kavli Institute for Fundamental Neuroscience; Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, CA 94143, USA

3. Computational & Structural Chemistry, Merck & Co., Inc., West Point, PA 19486, USA

Funder

National Science Foundation Graduate Research Fellowship

Publisher

Future Science Ltd

Subject

Drug Discovery,Pharmacology,Molecular Medicine

Link

https://www.future-science.com/doi/pdf/10.4155/fmc-2020-0259

Reference20 articles.

1. Rethinking drug design in the artificial intelligence era

2. Learning Molecular Representations for Medicinal Chemistry

3. Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

4. Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

5. Kearnes S, Goldman B, Pande V. Modeling industrial ADMET data with multitask networks. arXiv:1606.08793 (2016).

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