View in EDS

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.

Saved in:

Bibliographic Details
Title:	FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.
Authors:	Burger PB; Avicenna Biosciences Inc., 101 W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States., Hu X; Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States., Balabin I; Avicenna Biosciences Inc., 101 W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States., Muller M; Avicenna Biosciences Inc., 101 W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States., Stanley M; Microsoft Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K., Joubert F; Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0001, South Africa., Kaiser TM; Avicenna Biosciences Inc., 101 W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States.
Source:	Journal of chemical information and modeling [J Chem Inf Model] 2024 May 13; Vol. 64 (9), pp. 3812-3825. Date of Electronic Publication: 2024 Apr 23.
Publication Type:	Journal Article; Research Support, Non-U.S. Gov't
Journal Info:	Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1549-960X
DOI:	10.1021/acs.jcim.4c00071