OHAMR Call for proposals 2026

General Information

• Type: Partner looking for project
• Organisation: University of Ljubljana, Faculty of Pharmacy
• Country: Slovenia (SI)
• Career stage: Other.

Research area

• Scientific area(s) of the call:
  1. Topic 1: Identify and develop new combination treatments using existing or innovative antimicrobials or antimicrobial with adjunctive treatments to extend drug efficacy and combat resistance.
• One Health Setting:

  H - Human Health

  A - Animal Health

• Keywords:

  Machine learning Chemometrics Virtual screening Organic synthesis

• Brief description of your expertise / expertise you are looking for:

  I am an expert in computer-aided drug design (CADD), machine-learning-driven drug discovery, and synthesis of small-molecule antibacterial and anticancer agents. Key capabilities relevant to OHAMR that I and our research group can offer are: • Structure-based and AI-supported design of enzyme inhibitors targeting essential bacterial pathways and major resistance pathways (eg. Efflux pumps) • High-throughput virtual screening (molecular docking, pharmacophore modelling, physics-based and ML scoring functions) of millions of compounds against multiple bacterial targets simultaneously. • Advanced target-druggability analysis to predict potential binding sites. • Robust medicinal chemistry platform for rapid hit-to-lead optimization and synthesis of novel chemical series and their chemical analysis • Medium-throughput phenotypic screening platform capable of testing several thousand compound combinations against multi-drug-resistant clinical isolates (ESKAPE pathogens, Mycobacterium tuberculosis, etc.) using checkerboard, time-kill, and resistance-passaging assays. • Strong data-analysis and machine-learning pipeline (random forest, deep neural networks, active-learning loops) to identify synergistic combinations, predict synergy scores, and discover non-antibiotic adjuncts or resistance breakers from large screening datasets. We are therefore ideally positioned to contribute to work packages on: • In silico identification and prioritization of synergy partners or efflux-pump inhibitors • Design and synthesis of novel adjuvans molecules or standards for compound testing • Experimental validation of combinations and mechanistic elucidation of synergy • AI-driven optimization of combination regimens

• Brief description of your project / the project you would like to join:

  I am seeking to join a pre-clinical consortium under OHAMR 2026 Topic 1 (Identify and develop new combination treatments… ). A project I feel fit to help with my expertise would: • Include a discovery/optimisation work package for new chemical entities or repurposed adjuncts (efflux inhibitors, β-lactamase inhibitors, metabolism modulators, anti-virulence compounds, etc.) • Require integration of AI/ML and high-throughput experimental synergy screening to accelerate combination discovery • Plan phenotypic screening against Gram-negative (e.g., carbapenem-resistant Acinetobacter, Pseudomonas, Enterobacterales) or MDR/XDR Mycobacterium tuberculosis clinical isolates

Rok Frlan

• Organisation: University of Ljubljana, Faculty of Pharmacy
• Position: dr
• E-mail: rok.frlan@ffa.uni-lj.si
• Web: www.ffa.uni-lj.si/en/news-and-information/employees/frlan-rok