how AI can transform drug discovery by identifying TRAF2- and NCK-interacting kinase( TNIK) as a novel target for IPF. Insilico Medicine analysed large-scale biological data to uncover TNIK as a previously overlooked but highly relevant driver of fibrosis.
This AI-driven insight enabled rapid development of a first-in-class therapy, reducing the timeline from target identification to clinical candidate from five to seven years to under three years. Early clinical data has shown encouraging safety and efficacy signals, demonstrating how AI can not only accelerate discovery but also improve the quality of the targets and candidates entering development.
Q. WHY IS IT IMPORTANT FOR AI TO BE INTEGRATED DIRECTLY INTO AUTOMATED PHYSICAL LABORATORIES? WHAT DOES THE CONVERGENCE OF WET LABS, IN-SILICO LABS AND AI LOOK LIKE IN PRACTICE?
» Most organisations still think about AI as something separate from the laboratory. In reality, we see best results when AI is tightly integrated into physical experimentation. For example, Capgemini’ s deep tech powerhouse Cambridge Consultants combines wet lab capabilities within in-silico experimentation environments that allow organisations to define, run and refine experiments much faster and with fewer required data points.
102 August 2026