Small Molecule Design
Generative design of novel small molecules optimized for a chosen biological target.
neoralabDiscovering molecules against antimicrobial resistance.
Bacteria evolve. Discovery must evolve faster.
Antimicrobial resistance was directly attributable to 1.27M deaths in 2019, and associated with 4.95M deaths overall — driven by pathogens that keep defeating conventional treatment paths.
Pseudomonas aeruginosa
A difficult ICU pathogen with resistance patterns that constrain standard treatment options.
Acinetobacter baumannii
A recurrent source of bloodstream and respiratory infections in vulnerable patients.
We start where resistance hurts most.
Structure-based small-molecule optimization against priority pathogens. We choose each target because it combines:
- Urgent, unmet clinical need
- Rich structural data our workflow can exploit
- A straightforward path to experimental validation
Three forces make structure-based AI discovery viable now.
Billions spent, decades lost.
Traditional discovery is slow, costly and fragmented — molecular discovery still runs on trial and error.
The AI stack is production-ready.
Agentic AI, docking and molecular dynamics now run as reproducible, chained workflows.
Pharma needs cost & time out.
R&D teams are under pressure to design candidates against high, unmet medical need.
Drug discovery is slow, costly, and fragmented.
Our agents turn biological targets into testable small-molecule candidates.
We build and use proprietary AI agents to coordinate literature, structural data, molecular design, docking, and validation in traceable workflows. Every research program expands a reusable scientific memory and sharpens the next discovery cycle.
How our discovery approach compounds knowledge
Scientific memory layer
A second brain — every project builds reusable, searchable knowledge.
Agentic workflow engine
Multi-agent systems coordinate literature, structures, tools and reporting.
Explainable discovery
Rationale, risks, analogs, sources and next experiments — every scientific decision stored.
Closed-loop learning
Experimental feedback refines ranking and decisions with each completed project.
AMR Discovery
Antibiotic candidate discovery against resistant pathogens, from target to lead.
Agentic Discovery
Purpose-built agents coordinate literature, structures, docking, and molecular dynamics.
Translational Prioritization
Explainable scoring turns computational evidence into candidates ready for experimental validation.
Projects Portfolio
Selected neoralab research programs in antimicrobial resistance and small-molecule drug discovery, powered by our internal agents from target selection to candidate prioritization.
Antibiotics Discovery
We design small molecules to tackle antimicrobial resistance. Our internal agentic workflows combine generative models, structure-based docking, and in-silico validation to accelerate early discovery and prioritize compounds with real translational potential.
Agent-Driven Target-Oriented Small Molecule Design
Specialized AI agents move from a biological target to candidate molecules, coordinating generative design, structure-aware evaluation, and iterative refinement.
Research & News
Publications, research updates, and perspectives from across our work in biotechnology, AI, and drug discovery.
Open research
Now open to verified researchers.
We publish the data, benchmarks and validations behind our discoveries. Researchers in academia, non-profits and industry can now request a neoralab account and use the platform in their own work.
Request access
Why neoralab
We unite biotechnology, agentic AI, and a multidisciplinary team around a clear vision: discover better antibiotics faster and turn computational insight into experimentally testable candidates.
Who We Are
We are a biotech team of scientists, AI engineers, and builders focused on discovering new therapies against antimicrobial resistance.
Let's advance antibiotic discovery.
We welcome conversations with biotech and pharma teams, research partners, and investors working to overcome antimicrobial resistance.
Discuss an AMR opportunity
or write to [email protected]