Under the expanded agreement, the companies will leverage generative AI to jointly develop an additional innovative candidate with defined properties for challenging neurological diseases and advance it to the preclinical candidate (PCC) stage to address differentiated clinical needs. By exploring molecules with distinct profiles, the partners aim to provide a broader and more precise set of therapeutic options for highly challenging nerve system-related conditions, helping mitigate late-stage development risk and improve potential clinical benefit. The expanded collaboration carries additional potential deal value of up to US$94.75 million, with Insilico eligible to receive near-term and milestone payments from Tenacia.

In a new study published in The Lancet Digital Health, scientists at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute have discovered that the brains of people who experience severe physical impairment after a stroke may reorganize themselves in unexpected ways, showing signs of “younger” brain structure in undamaged regions as they adapt to injury. The international research effort is part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery Working Group, which analyzed brain scans from more than 500 stroke survivors across 34 research sites in eight countries. Using deep learning models trained on tens of thousands of MRI scans, the researchers estimated the “brain age” of different regions in each hemisphere to see how stroke damage affects brain structure and recovery. The research team used an advanced form of artificial intelligence known as a graph convolutional network to predict the biological age of 18 brain regions from MRI data. When the team associated these measurements with motor performance scores, they found a striking pattern: stroke survivors with severe movement deficits, even after more than 6 months of rehabilitation, showed younger-than-expected brain age in regions opposite the lesion, particularly within the frontoparietal network, a key system involved in motor planning, attention, and coordination. By observing how patterns of brain aging and reorganization develop over time, clinicians might be able to customize interventions based on each patient’s unique neural adaptation process, ultimately improving recovery outcomes and quality of life in the near future.