Insilico Medicine’s Post

2025 could be a pillar year for AI-driven lab robotics in pharma and biotech, and here is why 🤖👇 📑 If you missed it, this recent publication from one of our portfolio companies, Insilico Medicine, is worth a close read (link in the comments). It describes how their AI platform was used to discover a novel TNIK inhibitor (INS018_055), a drug candidate with senomorphic activity targeting cellular senescence and SASP. But the core story isn’t just about the discovery of a new aging-related drug candidate, what’s more important—and in my view, a real paradigm shift—is the infrastructure and workflow behind it. ⚙️ Insilico demonstrates a fully integrated AI–robotics loop for drug discovery: 👉 Multi-omic data generation (genome, transcriptome, methylome) 👉 AI-driven target discovery via their PandaOmics platform 👉 Automated compound screening, phenotypic profiling, and wet-lab validation 👉 Real-time feedback and reinforcement learning to refine the models 👉 All executed within a modular six-unit robotics facility, handling everything from cell culture and compound management to high-content imaging and next-gen sequencing This kind of closed-loop system effectively collapses what used to take years into weeks. It's scalable, reproducible, and designed for continuous learning—a foundational shift in how we approach drug development. These end-to-end AI-native infrastructures are still rare. Insilico is one of only a few companies operating at this level, but I believe this is the blueprint for the future of drug discovery. 🚀 Kudos to Alex Zhavoronkov, Alex Aliper and the entire Insilico team for building and executing on a vision that brings true vertical integration to AI-powered drug discovery! 🙏 Image credit: Insilico Medicine