BPS2026 – AI, high-throughput molecular dynamics and experimental biology for the discovery, optimization, and characterization of antimicrobial peptides

The rapid rise of antimicrobial resistance (AMR) demands alternatives to conventional antibiotics. Antimicrobial peptides (AMPs)—with broad-spectrum activity and multi-target mechanisms—are a viable option. We integrate artificial intelligence (AI), molecular dynamics (MD), and state-of-the-art measurements to accelerate AMP discovery and optimization. Our pipeline couples a generative model (decoder-only transformer) for de novo peptide design with predictive models (encoder-only transformers) that classify antimicrobial activity and predict minimum inhibitory concentrations (MICs) against priority pathogens. We then apply reinforcement learning to optimize potency, selectivity, and solubility in a closed loop. Shortlisted peptides are validated experimentally for MICs and membrane-disruptive mechanisms, providing feedback to the AI models in iterative fashion. To provide physics-based insight, we initially perform high-throughput MD simulations based on the highly mobile membrane-mimetic (HMMM) model—which replaces lipid tails with an organic solvent phase to accelerate peptide-membrane association and insertion. The initial peptide structures were obtained using ESMFold. This way, we are able to observe membrane insertion within hundreds of nanoseconds (sub-μs), yielding actionable mechanistic readouts. The systems are subsequently converted to full atomistic membranes to refine interactions at all-atom resolution. All simulations were run in high throughput on HPC resources.