First RP patient dosed in our Phase 1B/2 w/ OCT-980 Rho corrector for retinitis pigmentosa. Really thankful to the clinicians, staff, CRO partners & Octonauts for helping get this drug to patients w/ no other approved treatments. Exciting times! octant.bio/news/octant-annou…

the next billion-dollar pharma company will come from making everyone else's discoveries cheaper, faster, easier to manufacture, easier to validate, or easier to deploy. every platform shift creates infrastructure companies worth more than the applications they enable. biology won't be any different. bio/acc

A counterfactual to demonstrate @Ronalfa’s point: In 2026, BMS and Merck both read out 1L NSCLC data for their PD1 drugs, Opdivo and Keytruda. BMS tried to go after a bigger population (>5% PDL1 expression) whereas Merck was more selective (>50% PDL1 expression). Merck’s trial blew BMS’s out of the water, with a 0.6 v 1.02 OS hazard ratio. At least partly due to this head-to-head dualing trial readout, Merck’s Keytruda became a $30B drug with BMS’s Opdivo became a distant 2nd-place in the PD1 race with ~$10B. The NPV lost on that one (hindsight 20:20) poor clinical trial design decision might be on the order of $30B and this is the exact decision that a strong translational ai can help with! Cc @alexkesin for his great Approved Keytruda episode

Today, we are welcoming Ashish Bhandari to the NOETIK team as Head of Portfolio Strategy. Ashish will help shape and prioritize our pipeline and strategic direction as we continue to advance our biological foundation models toward clinical impact. Ashish joins us from Recursion, where he most recently served as Executive Director, Head of Portfolio Strategy. Over seven years there, his work spanned discovery, translational medicine, clinical development, pipeline strategy, early commercial strategy, and business development. Before Recursion, he spent nearly five years in life sciences competitive intelligence at Pointe Advisory. Ashish has built his career on turning scientific evidence into decisions people can trust and act on. His deep experience across science and strategy will strengthen our next phase of growth as we work to improve clinical success in oncology.

muse announcements so far - free for users, but may eventually take a cut of transactions - adding computer use - muse email addresses coming soon - walmart, best buy, gap, sephora, instacart, and more integrating with muse - integrations with box, github, granola, notion - 1500+ applications for connector platform (lovable, eleven labs, and more)

Today we are announcing a $311M Series E to bring pharma into the 21st century. Enveda started with a thesis: a molecule that evolution has already refined is a better place to begin than one designed from scratch. This year, two first-in-class molecules discovered by Enveda (ENV-294 and ENV-308) had positive readouts in humans. Many more are behind them. We are grateful to @CatalioCapital for leading this round, to our new investors Durable Capital Partners, @ICONIQCapital, @lightspeedvp, Surveyor Capital (a Citadel company), accounts advised by @TRowePrice, @digitalisvc and Alderline Group, and to @BaillieGifford, @PremjiInvest, @trueventures, @KinnevikAB, @FPVventures, @_DimensionCap, @lifeforcecap and @Lux_Capital for continuing to back us. The best is yet to come… Read more: businesswire.com/news/home/2… #Biotech #AI #LearningfromLife

I am not very good at signal over zoom. I remember first meeting Gabe over zoom and then meeting him in nyc socially and thinking, “shit, I think he’s good” lesson repeatedly relearned

McKinsey out today on AI drug discovery — "focusing on what matters most." The framing is right. Most AI in pharma is still applied to the easy parts. The hard part is target biology and clinical translation. mckinsey.com/industries/life…

In light of the progress in mathematics, we at Edison Scientific and FutureHouse have assembled a set of Millennium Problems for Biology. They are chosen to be very hard to solve but very easy to validate in a simple laboratory environment. Any of these, if solved, would mark a major advance in biotechnology, and most of them would contribute materially towards curing disease. These are, in some sense, the “last reasonable eval” for AI in biology. This was work primarily by @MichaelaThinks and myself, with contributions from many others. Short descriptions below. The full descriptions of the problems with acceptance criteria are at the Bio Millennium Problems website, linked in the next post. Share more if you have ideas. If they meet our criteria, we’ll add them to our list (with attribution and permission).