AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials.
Silvia GervasoniGiuliano MallociAndrea BosinAttilio Vittorio VargiuHelen I ZgurskayaPaolo RuggeronePublished in: Scientific data (2022)
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.
Keyphrases
- molecular dynamics
- density functional theory
- drug resistant
- molecular dynamics simulations
- public health
- multidrug resistant
- single molecule
- big data
- acinetobacter baumannii
- electronic health record
- depressive symptoms
- gram negative
- molecular docking
- small molecule
- adverse drug
- positron emission tomography
- machine learning
- computed tomography
- artificial intelligence
- emergency department
- silver nanoparticles
- cystic fibrosis
- high throughput
- pseudomonas aeruginosa
- deep learning
- genetic diversity
- quantum dots