on biotech platform strategy. in a new essay, @ElliotHershberg and I riff on biotech platform typology and partnering dynamics. we unpack why many biotechs pivot from external partnerships to internal programs, and why that trend might change in the near future. we start with a

a core tension in applying "virtual cell" models to therapeutic development is the mismatch between the scale at which we measure biology and the scale at which we intervene. we often measure at the cell level (eg single-cell RNA-seq), but we treat at the tissue or organ level.

RT @JTRobinsonLab: Are we headed toward a technology revolution in therapeutic BCI?. I just wrote this piece about why I believe neurotech….

one frustrating aspect of drug development is that the incentives favor “me-too” programs that chase already-validated targets (e.g., TYK2, GLP-1, PD-(L)1). this is in large part due to the fact that patents protect molecules, not the biology behind them. once a target shows.

if, for almost any real-world problem, a rational person would rather consult AI than any single human, then AI has crossed the practical threshold we meant when we coined the term AGI.

RT @johncumbers: What makes a partnership between pharma and computational drug discovery firms actually work?. This session brings togethe….

agreed. and the best way to improve the biotech talent bottleneck is to give promising, emerging management teams the opportunity to prove themselves vs concentrating most of the capital behind a relatively small number of established executives.

2024 was a huge year for biotech megarounds, with almost 100 startups raising $100M+ financings. shifting so heavily toward these massive deals—and away from smaller, disciplined rounds—is not a good thing for biotech. there are important downsides and knock-on effects for an

love this framing of venture feedback loops as an RL problem. vc is fundamentally a “wicked” learning environment - the connection between cause (the decision to invest) and effect (investment returns) is complex, feedback is delayed or misleading, and patterns that worked in the.

🫡 congrats @zavaindar @nanli @AGoulburn.

awesome opportunity for scientist-entrepreneurs that want to build a neuro startup. join us!.

press release: WSJ: Endpoints:

humbled to announce KdT IV, an oversubscribed $100M+ fund dedicated to supporting early-stage, science-driven companies. at KdT, we believe the most iconic companies of our time are those that leverage scientific breakthroughs to build enduring businesses with significant

it's our second birthday at decoding bio. help us improve!. we're conducting a short survey of our community, only takes a few minutes to complete:

Recursion's clinical trial readouts are not a referendum on the value of AI in drug discovery. some thoughts.

RT @NVIDIAHealth: NVentures backed @Terray_Tx, has unveiled COATI-LDM—a groundbreaking #genAI model for designing small molecules with prec….

if you can compress biology, you can predict biology. one way to think about the power of LLMs is as extremely efficient data compression algorithms. at their core, LLMs learn to map high-dimensional input data (e.g., a biological sequence such as a protein) to lower-dimensional

spotify: apple:

the KdT podcast is back to unpack the first biotech IPO of 2024 - CG Oncology. in The World in a Grain of Sand, we break down an S1 from a recent biotech IPO and share insights on the science, business models, and financials. in this episode, Cain McClary and Phil Grayeski

the 2024 edition of the Decoding Bio report is live!. we analyzed many of the most impactful recent papers in AI, biosecurity, cell-based medicines, immunology, and biotech software. from the jump, a core goal of Decoding Bio has been to clearly communicate why these

RT @owl_poster: A primer on molecular dynamics (MD). this covers a lot of the absolute basics of MD in a way that….