{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gervasoni S"],"funding":["U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)","NIAID NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases","Fondazione Banco di Sardegna"],"pagination":["148"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8976083"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials."],"journal":["Scientific data"],"pubmed_title":["AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials."],"pmcid":["PMC8976083"],"funding_grant_id":["R01 AI136799","R01AI136799"],"pubmed_authors":["Zgurskaya HI","Malloci G","Bosin A","Ruggerone P","Gervasoni S","Vargiu AV"],"additional_accession":[]},"is_claimable":false,"name":"AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials.","description":"Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2025-04-25T18:03:01.449Z","creation":"2025-04-25T18:03:01.449Z"},"accession":"S-EPMC8976083","cross_references":{"pubmed":["35365662"],"doi":["10.1038/s41597-022-01261-1"]}}