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.
MRSA (S. aureus)
A persistent hospital-acquired threat, especially when severe infections turn systemic.
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 — MRSA first.
Structure-based small-molecule optimization against priority pathogens, with several more on the list. We lead with MRSA 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.
neoralab is not another molecule generator. It is an AI-native discovery operating system.
It connects models, tools and data into one workflow, explains and stores every scientific decision, and delivers traceable, validation-ready candidates. With each completed project it builds knowledge — compounding its value over time.
Four layers of compounding advantage
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.
Custom AI Workflows
Bespoke agentic pipelines for molecular dynamics, docking, and candidate optimization.
Tell Us Your Idea
Bring your target or dataset and we build the discovery workflow around it.
Projects Portfolio
Selected research programs and collaborations in antimicrobial resistance and small molecule drug discovery, where our platform supports the full path from target to candidate.
Antibiotics Discovery
In collaboration with academic researchers, we design AI-generated small molecules to tackle antibiotic resistance. Our platform integrates generative models, structure-based docking, and in-silico validation to accelerate early discovery and prioritize compounds with real translational potential.
Target-Oriented Small Molecule Design
We design small molecules starting from biological targets, combining generative AI with structure-aware evaluation to rapidly explore and refine candidate chemical space.
Research & News
Peer-reviewed publications and preprints from our researchers, advancing AI for drug discovery and biomedical data analysis.
LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design
Visual Exploratory Data Analysis for Copy Number Variation Studies in Biomedical Research
miRNAs Copy Number Variations Repertoire as Hallmark Indicator of Cancer Species Predisposition
VarCopy: a Visual Exploratory Data Analysis Platform for Copy Number Variation Studies
Beyond papers, we release the data, benchmarks, and validations behind our discoveries as open-source discovery — explore what we make public.
Explore open research
Why neoralab
More than an AI platform, NeoraLab is a “Smart-Pharma” powerhouse. By integrating a dedicated discovery ecosystem with custom-built AI, we deliver advanced, data-driven solutions that redefine the frontier of life sciences.
Who We Are
We are a team of scientists, engineers, and builders focused on applied AI for life science research.
Marco Ferrarini
CSO
Master Degree in Medical Biotecnology
Alfredo Boracchini
CFOCo-Founder
Engineer and Entrepreneur
Plantfoodomics Lab
University Cattolica
Research Group, Distas Department
Contact us
Tell us about your target, dataset, or workflow and we'll get back to you within 2 business days.