We help life science teams move faster with AI that turns complex data into actionable workflows across discovery, omics, and translation.
We are a team of scientists, engineers, and builders focused on applied AI for life science research.
We help biotech, pharma, and academic labs turn complex data into actionable insight by streamlining workflows, automating analysis, and supporting evidence-based decisions.
Our tools are designed to be practical and easy to adopt, integrating with existing data stacks rather than replacing them.
From early exploration to advanced analysis, neoralab helps teams move faster and keep the science at the center.
Alfredo Boracchini
Engineer and Entrepreneur
NeoraLab draws its name from the fusion of the Greek Neos (νέος, “new”) and Epochí (εποχή, “era”), signaling a transformative νέα εποχή (néa epochí) — a new era of scientific discovery for life science and biotech teams.
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.
Selected collaborations across drug discovery, spatial transcriptomics, and omics analytics where our platform supports discovery from data to candidate.
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.
Why we care: Antimicrobial resistance is a top global health threat, making routine infections and procedures riskier.[1] In 2019, bacterial AMR caused 1.27 million deaths directly, with 4.95 million associated deaths worldwide.[2] In the U.S., resistant infections cause at least 2.8 million illnesses and 35,000 deaths each year.[3]
Peer-reviewed publications and public communications on our AI research in life sciences, drug discovery, and biomedical data analysis.
2022-02-28
The study of Copy Number Variations (CNVs) is recently emerging as a hot topic for biomedical cancer research. While different data sources, websites, and tools concerning genomic CNVs have been made publicly available, CNV data is still a largely unexplored source of biological information, due to the limitations of c...
2022-06-10
Aging is one of the hallmarks of multiple human diseases, including cancer. We hypothesized that variations in the number of copies (CNVs) of specific genes may protect some long-living organisms theoretically more susceptible to tumorigenesis from the onset of cancer. Based on the statistical comparison of gene copy n...
2021-09-07
The study of such a complex phenomenon as cancer, which depends on several but unexplored and unclear factors, needs new ways to visualize, analyze and combine different data both on species characteristics and genes function. To this respect, we propose a novel platform, named VarCopy, supporting visual Exploratory Da...
We adapt the neoralab platform to your datasets, workflows, and infrastructure, delivering custom analysis, tooling, and collaboration support.
