Peer-reviewed publications, preprints, and public communications on our AI research in life sciences, drug discovery, and biomedical data analysis.
2026-03-04
The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, leading to low success rates and time-consuming workflows. Here, we present LLMsFold, a computational framework that combines Large Language Models (LLMs) and biophysical fo...
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...
Recognition of our AI workflow for molecule discovery and its translational potential.
co-developed with tacclabView update
Highlights from our recent pitch night and product roadmap discussion.
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How we apply AI workflows to support antibiotic resistance discovery research.
co-developed with tacclabView update