Machine learning approaches to study the structure-activity relationships of LpxC inhibitors.
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| Title: | Machine learning approaches to study the structure-activity relationships of LpxC inhibitors. |
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| Authors: | Yu T; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand., Chong LC; Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Beykoz, Istanbul, Türkiye., Nantasenamat C; Streamlit Open Source, Snowflake Inc., San Mateo, California 94402, United States., Anuwongcharoen N; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand., Piacham T; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand. |
| Source: | EXCLI journal [EXCLI J] 2023 Sep 05; Vol. 22, pp. 975-991. Date of Electronic Publication: 2023 Sep 05 (Print Publication: 2023). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: University of Mainz Country of Publication: Germany NLM ID: 101299402 Publication Model: eCollection Cited Medium: Print ISSN: 1611-2156 (Print) Linking ISSN: 16112156 NLM ISO Abbreviation: EXCLI J Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1611-2156 |
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| DOI: | 10.17179/excli2023-6356 |
