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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning

Published Online: 2022-05-23

Published Print: 2022-10

Authors

Rube, H. Tomas

Rastogi, Chaitanya https://orcid.org/0000-0001-9155-9341
Feng, Siqian https://orcid.org/0000-0003-0650-9134
Kribelbauer, Judith F. https://orcid.org/0000-0002-8072-7773
Li, Allyson https://orcid.org/0000-0003-2359-7703
Becerra, Basheer https://orcid.org/0000-0003-4403-7959
Melo, Lucas A. N. https://orcid.org/0000-0002-7563-7554
Do, Bach Viet

Li, Xiaoting

Adam, Hammaad H.

Shah, Neel H. https://orcid.org/0000-0002-1186-0626
Mann, Richard S. https://orcid.org/0000-0002-4749-2765
Bussemaker, Harmen J. https://orcid.org/0000-0002-7274-5277
Funding

Funding for this research was provided by:

Pharmaceutical Research and Manufacturers of America Foundation (Predoctoral Fellowship)

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM118336)

U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH106842)

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG003008)
License Information

Text and Data Mining valid from 2022-05-23

Version of Record valid from 2022-05-23
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Article History

Received: 6 June 2021

Accepted: 4 April 2022

First Online: 23 May 2022

Competing interests

: H.J.B., C.R. and H.T.R. have filed a patent application describing the design, composition and function of ProBound. The remaining authors declare no competing interests.

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