Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.
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| Title: | Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design. |
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| Authors: | Li J; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Zhang O; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Sun K; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Wang Y; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Guan X; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Bagni D; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Haghighatlari M; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Kearns FL; Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States., Parks C; Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States., Amaro RE; Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States., Head-Gordon T; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States.; Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States. |
| Source: | Journal of chemical information and modeling [J Chem Inf Model] 2024 Dec 23; Vol. 64 (24), pp. 9082-9097. Date of Electronic Publication: 2024 Jun 06. |
| Publication Type: | Journal Article |
| 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 |
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