AMR Accelerator – AI model to help identify new antibiotics

Advanced computational methods and machine learning could help reduce the high costs and complexity of antibiotic drug discovery. The COMBINE project has developed an Antimicrobial Knowledge Graph with models that can scan compound libraries to identify new antimicrobial compounds. The database and a machine learning model built using the knowledge are now published in the Journal of Chemical Information and Modeling.

Antimicrobial resistance (AMR) is rapidly depleting the number of useful antibiotics. At the same time, the development of new therapeutics has slowed down. As part of the COMBINE project, researchers at Fraunhofer ITMP in Hamburg, have led work within the AMR Accelerator programme to develop a machine learning (ML) model, trained on publicly available in vitro data. The AntiMicrobial Knowledge Graph (KG) represents a knowledge graph with the first ever MIC (minimum inhibitory concentration) aggregated dataset in a FAIR-compliant format. According to Yojana Gadiya, who coordinated this effort, the models are customizable and open source. They are also transparent, making it possible to decipher the physicochemical properties required for bacterial and fungal activity, supporting chemical optimization in antimicrobial drug discovery.

Making selections of compounds based on model predictions could eventually decrease the experimental cost associated with antimicrobial screening. As the model is pre-trained, it can be used to build a compound library from scratch with chemicals that have a higher tendency to demonstrate activity in vitro. It is also cost-effective: instead of using high-throughput screening of different compound libraries to identify an active compound, the model can identify a subset of compounds that are more likely to be active. By using the ML model in early compound screening, the authors showed that the cost associated with screening can be reduced substantially through the ML predictions of any compound libraries, by filtering them into smaller subsets with a higher probability of activity.

The coordination and support across the programme are ensured by the COMBINE project (“Collaboration for prevention and treatment of MDR bacterial infections”). BIOCOM Interrelations GmbH is leading one of COMBINE’s key objectives: facilitating communication among AMR Accelerator partners, disseminating news and results, and increasing visibility and outreach to key stakeholders in the field by organising events, issuing news releases, and maintaining communication channels.

Read more: https://amr-accelerator.eu/publications/