AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics

Abstract

ABSTRACTDiscovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in the holistic and efficient tool specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative deep learning-enabled molecular generation and automated machine learning based-antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole cell-based antibiotic screening and design. It is open-source thus allows for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/8366085).