Thank you to Columbia’s IICD team for hosting Harry yesterday. Watch the conversation on what it takes to make cancer models useful in the clinic below.
Thank you to Columbia’s IICD team for hosting Harry yesterday. Watch the conversation on what it takes to make cancer models useful in the clinic below.
Thank you to Columbia’s IICD team for hosting Harry yesterday. Watch the conversation on what it takes to make cancer models useful in the clinic below.
🧬 Thank you to Harry Kabodha, co-founder of @var0sync, for yesterday’s IICD Special Seminar!
Harry discussed the challenges of translating computational cancer models from cellular data into clinically meaningful insights.
Watch here: https://t.co/Q1yrG1ImSU
📢 Join IICD on Oct. 21 at 2 PM ET for a special seminar with Harry Kabodha, co-founder of @var0sync:
“From Cell States to Patients: Building Cancer Models That Survive Clinical Translation”
📍 Schermerhorn 603 Hall + Zoom
🔗 https://t.co/XyI5hyCeXQ
📢 Join IICD on Oct. 21 at 2 PM ET for a special seminar with Harry Kabodha, co-founder of @var0sync:
“From Cell States to Patients: Building Cancer Models That Survive Clinical Translation”
📍 Schermerhorn 603 Hall + Zoom
🔗 https://t.co/XyI5hyCeXQ
We’re looking for PhD students and postdocs to help answer a difficult question: can AI tell when an old drug failure still matters?
The work is remote, takes three to five hours, and reviewers will be named in the paper and dataset.
If it fits your background, email [email protected].
Advances in medicine begin as advances in understanding.
Varosync is building failure-aware intelligence for drug development, spanning computation and experimental science. We are growing the team, with five open roles across research and commercial partnerships.
If you want to work on problems that have absorbed entire careers, we would like to hear from you.
View open positions: https://t.co/Qv1Vwqe8fy
Advances in medicine begin as advances in understanding.
Varosync is building failure-aware intelligence for drug development, spanning computation and experimental science. We are growing the team, with five open roles across research and commercial partnerships.
If you want to work on problems that have absorbed entire careers, we would like to hear from you.
View open positions: https://t.co/Qv1Vwqe8fy
Pharma buries its failed trials. That is the most valuable data it owns.
A failure is a map: where the mechanism breaks, which patients, which toxicity, what dose.
We mapped the full PI3K inhibitor class from the public record alone.
What would actually move pharma to share what failed?
@ylecun@Dan_Jeffries1 Exactly. LLMs describe molecules, they don’t model them. You can’t tokenize your way past thermodynamics. We’re building the failure-aware simulator for drug development: physics and causality under physical law, not token statistics. The empirical floor for $1B decisions.
@drfeifei LLMs describe molecules: they do not model them. We are building the failure-aware simulator for drug development. It operates on physics and causality under physical law, not token statistics.
This is the empirical floor for $1B decisions.
The industry studies the 10 percent that succeeded. We model the 90 percent that failed. Mapping pharmacology and exposure liability against the clinical failure record, we define the risk floor.
Part of the @LartaInstitute#HealLA cohort:
https://t.co/egBBhX8KD4