Insilico Medicine will present the first clinical findings for ISM6331, its AI-designed pan-TEAD inhibitor, during a Rapid Oral session at the European Society for Medical Oncology (ESMO) Congress 2026, taking place October 23–27 in Madrid. The presentation will report initial safety, pharmacokinetic and antitumor findings from a global, multicenter Phase I study in patients with mesothelioma and other advanced solid tumors.

Designed with support from Insilico’s generative chemistry platform Chemistry42, ISM6331 is an investigational oral small molecule that inhibits all four TEAD family transcription factors, disrupting tumor-promoting signals associated with Hippo pathway dysregulation. The program has received U.S. FDA Orphan Drug Designation and Fast Track Designation for mesothelioma.

ISM6331 reflects Insilico’s strategy of using AI to develop precisely designed medicines against difficult-to-treat, age-associated diseases. The presentation will take place Sunday, October 25, from 8:30–10:00 a.m. CEST in the Cordoba Auditorium – Hall 4.

Researchers identify a brain circuit that normally supports flexible behavior, but is recruited by cocaine to drive increasingly rigid and repetitive behavior.  The findings show that shifting the balance between opposing neural pathways can either promote behavioral rigidity or, conversely, rapidly interrupt cocaine driven repetitive behavior and restore a broader range of actions, offering new insight into the brain mechanisms underlying compulsive and stereotyped behavior.

Is a dog’s bark simply an expression of emotion, or does it contain specific information that humans cannot understand? Artificial intelligence (AI) can already analyse thousands of animal sounds and identify patterns that would go unnoticed by the human ear. But researchers are asking a far more ambitious question: could technology one day help us understand what animals are actually saying?

With the widespread application of space traveling wave tube amplifiers (TWTAs) as core final-stage amplification devices in satellite communication systems, their operational reliability directly affects satellite signal transmission capability, and any failure may even lead to service interruption. Currently, fault statistics for space TWTAs are recorded in text form, encompassing a large number of fault phenomena, causes, and corresponding solutions. However, as the volume of fault events continues to grow, traditional analysis methods relying on manual statistics and empirical judgment suffer from low efficiency and lack of knowledge reasoning capabilities, making it difficult to comprehensively analyze multi-dimensional fault information. Although artificial intelligence technologies, particularly knowledge graphs (KGs) and large language models (LLMs), have demonstrated substantial potential in fault diagnosis, research on effectively integrating these two approaches and applying them to the specific fault diagnosis of space TWTAs remains unexplored. Therefore, how to construct an intelligent diagnosis framework capable of automatically understanding natural language fault descriptions and performing knowledge reasoning has become a key challenge in improving the troubleshooting efficiency and decision support capabilities for space TWTAs.