The pharmaceutical companies are combining HDAC expertise and mechanistic AI to explore potential treatments for neuromuscular, fibrotic, and rare diseases
San Francisco and Milan, 6 October 2026 – Drug discovery is becoming increasingly data-driven as pharmaceutical companies look for better ways to identify promising treatments before they enter costly clinical development. In a new collaboration, Italfarmaco and VeriSIM Life are combining pharmaceutical research expertise with artificial intelligence to discover potential therapies for neuromuscular, fibrotic, and rare diseases.
The partnership focuses on histone deacetylase, or HDAC, biology. HDACs are proteins involved in processes that influence how cells function and how certain genes are regulated. Researchers have been studying HDAC inhibitors for years, with potential applications across several diseases. Italfarmaco has established expertise in this area and already has experience developing HDAC-targeted medicines. Its lead HDAC inhibitor, givinostat, has received approval in the United States and Europe for Duchenne muscular dystrophy in patients aged six and older.
Under the new collaboration, the companies will work on novel small molecule compounds designed to target a selected HDAC. The research will combine Italfarmaco’s experience in HDAC biology, medicinal chemistry, translational research, and clinical development with VeriSIM Life’s AI-enabled drug discovery technologies. The goal is to identify and optimize compounds that show not only activity against the intended target but also characteristics that could support their progression toward clinical testing.
A central part of the collaboration will be VeriSIM Life’s BIOiSIM and AtlasGEN platforms. BIOiSIM is designed to evaluate drug candidates across areas such as pharmacology, drug exposure, safety, biomarkers, and differences between patients. AtlasGEN is used to generate and optimize new chemical compounds that can be synthesized and tested. Together, the platforms are intended to help researchers narrow down potential candidates earlier in the discovery process.
This approach reflects a broader shift in pharmaceutical research. Traditional drug discovery can require years of laboratory work before researchers know whether a candidate has the characteristics needed for further development. AI and computational biology are increasingly being used to examine large amounts of biological and chemical information, helping research teams make more informed decisions during the earlier stages of discovery.
For diseases with limited treatment options, improving that process could be particularly valuable. Neuromuscular and rare diseases often involve complex biological mechanisms and relatively small patient populations, creating additional challenges for researchers. Fibrotic diseases can also affect multiple organs and involve complicated changes in tissue. The collaboration is therefore aimed at exploring new therapeutic possibilities in areas where additional treatment options are needed.
The partnership has a potential value of more than $100 million. VeriSIM Life is eligible for technology service fees as well as development, regulatory, and commercial milestone payments and royalties on potential future products. The companies have not disclosed a specific drug candidate or clinical program from the collaboration, meaning the work remains at the discovery stage.
For Italfarmaco, the agreement expands its work in HDAC research and supports its efforts to develop more selective, next-generation HDAC inhibitors. The company is also researching treatments for neuromuscular disorders, fibrosis, cancer, and other rare diseases. For VeriSIM Life, the partnership represents another opportunity to apply its mechanistic AI approach to pharmaceutical research and work with an established drug developer.
The collaboration highlights how pharmaceutical research is gradually moving toward a combination of laboratory science and computational intelligence. AI does not replace biological research or clinical expertise, but it can help researchers evaluate possibilities, prioritize compounds, and understand potential risks earlier. As these technologies mature, partnerships that bring together established drug development knowledge and advanced computational methods could become an increasingly important part of the search for new medicines.
