Today, insitro announced a strategic collaboration with @AstraZeneca for the AI-enabled discovery of novel therapeutics for metabolic dysfunction-associated steatohepatitis (MASH) and cardiomyopathies. insitro will receive $40 million upfront and is eligible for up to approximately $1.46 billion in potential, discovery, development and commercial milestones, plus tiered royalties. Both diseases are defined by the problem the collaboration is built to address. In each case, a single diagnostic label spans patients whose underlying biology differs. The heterogeneity across these diseases has made them difficult to treat, and the condition under which insitro’s machine learning capabilities on human data is most useful. Under the agreement, insitro will identify causal disease biology in both indications and advance AstraZeneca-nominated targets from discovery through to development candidate; AstraZeneca will lead clinical development, regulatory work, and commercialization. “Partnering with AstraZeneca allows us to pair our deep biological precision with their deep pharmaceutical expertise and scale with potential for accelerated advancement of novel programs through development and to patients.” — @DaphneKoller, founder and CEO of insitro Sharon Barr, Executive Vice President and Head of BioPharmaceuticals R&D, AstraZeneca: "Partnering with insitro, we are using AI to identify the biological drivers of disease to support the rapid discovery of high-quality novel molecules, advancing our ambition to deliver the next generation of medicines to patients faster.” Read the announcement: insitro.com/news/insitro-ann… More on our approach to causal biology: deepphenotype.substack.com #AIDrugDiscovery #MASH #Cardiomyopathies

@insitro is proud to announce that biotech companies in the Lilly TuneLab™ network can now deploy advanced machine learning models built by insitro with @EliLillyandCo to predict in vitro and, critically, in vivo properties of small molecules — representing collective progress advancing the field of AI drug discovery on behalf of patients. We're discussing the news live at SF Tech Week today, where @mmrozenman, insitro CFO and CBO, joins Aliza Apple, PhD, Vice President of Catalyze360 AI/ML and Global Head of Lilly TuneLab, at the @a16zBioHealth & Lilly TuneLab biotech breakfast. In advance of the discussion, Mary explained, “We built insitro to make biology an AI problem, with scaled data and models at the frontier. Our in-house data scaling helps us predict which biology we should target to impact disease in vivo. Today’s announcement with Lilly takes the next step. Now we’re predicting how a molecule will behave in vivo so we can make better decisions earlier in R&D by trading upfront compute for downstream conviction. We are thrilled to ship these models on TuneLab to enable this way of working across their ecosystem.” Trained on multi-species preclinical data that Lilly generated over decades of research from hundreds of thousands of unique molecules, the models offer a powerful alternative to today’s industry standard method of extrapolation from in vitro assays, providing an enterprise-grade capability traditionally available only to organizations with the scale to generate such data volumes themselves. The models will be updated as the dataset continues to expand. "The question of a molecule's in vivo behavior has been an empirical question, answered experimentally, slowly, one compound at a time. These models make it a computational question first," said @DaphneKoller, PhD, founder and CEO of insitro. The in vivo PK models are a critical component of our TherML™ platform for small molecule discovery, alongside machine learning and physics-based in silico screening, affinity models powered by proprietary DNA-encoded libraries, and an active-learning medicinal chemistry engine. → Read our official announcement: insitro.com/news/insitro-des… → Learn more about how the models work: insitro.com/news/insitro-lil… #SFTechWeek #AI #ML #LillyTuneLab #drugdiscovery

Three panel discussions, one week. At @SFTechWeek 2026, insitro will take part in critical industry conversations focused on moving from predictive models to medicines that work. 1️⃣ Monday, 4:00–6:30 PM — Molecules, Models & Agents → Dave Light, insitro's VP, Strategy & Corporate Development, joins the panel "Beyond the Demo: AI Capabilities That Actually Deliver in Early R&D," alongside FutureHouse, Tempus AI, and @10xGenomics, moderated by @LEK_Consulting. The event is hosted by @GitHub with @Microsoft, @NVIDIA, and @Baird. 2️⃣ Tuesday, 8:30 AM — Advancing Collaborative AI Drug Discovery → @mmrozenman, insitro's CFO & CBO, presents at a breakfast convened by @a16z and @EliLillyandCo's TuneLab team, alongside BigHat Biosciences and Flex Therapeutics. 3️⃣ Tuesday, 5:00–8:00 PM — Models to Medicine Series: From AI Drug Discovery to Patient Engagement → Mary joins the Models to Medicine panel, hosted by the @awscloud for Startups team, alongside Xaira Therapeutics, Abridge, and @a16zBioHealth. 📅 Full SF Tech Week calendar: lnkd.in/ekqySxCX #techweeksf #AI #drugdiscovery #sf

insitro is thrilled to announce the appointment of Hideo Makimura, M.D., Ph.D., as Chief Medical Officer, a pivotal step as we advance our therapeutic programs toward the clinic. "Hideo has spent his career using human genetics and biomarkers to guide and accelerate clinical programs from initial patient studies through late-stage development," said Daphne Koller, founder and CEO of insitro.” “As insitro heads into the clinic, I could not imagine a better partner to help us translate the insights generated by our platform into meaningful medicines for patients." Learn more: insitro.com/news/insitro-app…

The second issue of Deep Phenotype, insitro’s Substack publication on scaled biology and deep causality, is now live. In “Mapping Causal Human Biology: From Outcome to Mechanism,” insitro CEO and founder @DaphneKoller examines the problem at the center of drug discovery – moving from a desired clinical outcome to a mechanism precise enough to drug. Roughly nine in ten drugs that enter the clinic fail, most because the underlying biology was wrong. Today’s AI can detect associations at an extraordinary scale. Drug discovery, however, demands something harder: causal knowledge of what will happen when we intervene in a human system. Human genetics offers rare evidence from experiments nature has already run. Targets with genetic support are two to four times more likely to succeed in the clinic, yet only 3.6% have been pursued for any supported indication. Read Daphne’s new article to learn how insitro approaches this opportunity – and why getting the biology right matters far beyond the probability of success. “Every trial asks patients to accept real risk on an unproven hypothesis. When the biology was wrong from the start, that risk was taken for a drug that was never going to help them. Better target selection honors what patients put on the line.” ╰┈➤ Read part two of Daphne’s manifesto on Substack, “Mapping Causal Human Biology: From Outcome to Mechanism” on Deep Phenotype: deepphenotype.substack.com #DrugDiscovery #AI #Biology #CausalBiology

insitro founder and CEO @DaphneKoller recently stopped by The Information Bottleneck podcast to chat with hosts @ziv_ravid and @Hellisotherpe10 about causal biology and why AI in drug discovery is a fundamentally different problem than AI in language or vision. The conversation covers a lot of ground: → The bitter lesson worked for language and vision because scale and general methods met an internet's worth of data. Biology has no equivalent corpus — cells grow at the speed cells grow. AI cannot manufacture the missing data. → A drug isn't a pattern in data you already have, it's a prediction about an intervention nobody has run yet. This is a significant reason why 90% of drugs that reach the clinic fail, and only 22% of diseases have any approved treatment at all. → Plus: what it would take to build that missing corpus, and what real foundation models for biology would require. 🎧 Listen to the podcast at theinformationbottleneck.com… 📖 Daphne's article “Drug Discovery Has No Magic Wands," on insitro’s Substack, Deep Phenotype, covers similar territory in greater depth — read and subscribe: deepphenotype.substack.com/ #AI #DrugDiscovery #Causality #Biology