Uploaded April 2026 | Updated September 2026, 2 weeks ago
TL;DR: 95% of cancer treatments fail to pass clinical trials, but it may be a matching problem — if we better understood what patients have which tumors which will respond to which treatments, success rates improve dramatically and millions of lives can be saved — with the treatments we ALREADY have!
GSK recently signed a $50M deal for their technology that also includes an (undisclosed) long-term licensing deals for Noetik’s models. Most big AI plays in BioTech have focused on discovery, and usually result in an in-house development effort (meaning tools companies usually become drug companies). This deal stands out in that it is a software licensing deal, and represents a commitment to a *platform* rather than a *drug*. With attention on other software tools for drug development (see the [Boltz episode](https://www.latent.space/p/boltz) and Isomorphic for example), it is starting to look like the appetite of Pharma for biotech tools has finally started to grow. Why the sudden interest?
Timestamps:
(0:00) The challenges of starting a biotech lab and generating data from scratch.
(0:55) Introduction of Ron Alfa and Dan Bear from Noetik.
(4:09) The complexity of cancer: Why "curing cancer" is a misleading concept and the need for new, multimodal data.
(8:24) Identifying therapeutically relevant cancer subtypes to improve clinical trial success rates.
(11:27) The importance of intentional, high-quality data generation in AI biotech.
(17:09) Lessons learned from Recursion Pharmaceuticals regarding batch effects and data design.
(20:14) Introduction to Noetik's core data modalities: Pathology (H&E), spatial transcriptomics, and genomic alterations.
(30:15) The philosophy of self-supervised learning and avoiding bias from electronic health records.
(36:01) Translating latent space embeddings and patient clusters into actionable insights for pharma.
(41:40) Using PerturbMap and in-vivo mouse models to validate human AI predictions.
(53:38) Technical deep dive: The Tario transformer-based model and auto-regressive training objectives.
(1:00:26) The GSK partnership: Licensing OctoVC and the shift toward platform-based biotech deals.
(1:13:55) Advice for small biotech AI startups: Scaling, data conviction, and lessons from scientific history.
TL;DR: 95% of cancer treatments fail to pass clinical trials, but it may be a matching problem — if we better understood what patients have which tumors which will respond to which treatments, success rates improve dramatically and millions of lives can be saved — with the treatments we ALREADY have!
GSK recently signed a $50M deal for their technology that also includes an (undisclosed) long-term licensing deals for Noetik’s models. Most big AI plays in BioTech have focused on discovery, and usually result in an in-house development effort (meaning tools companies usually become drug companies). This deal stands out in that it is a software licensing deal, and represents a commitment to a *platform* rather than a *drug*. With attention on other software tools for drug development (see the [Boltz episode](https://www.latent.space/p/boltz) and Isomorphic for example), it is starting to look like the appetite of Pharma for biotech tools has finally started to grow. Why the sudden interest?
Timestamps:
(0:00) The challenges of starting a biotech lab and generating data from scratch.
(0:55) Introduction of Ron Alfa and Dan Bear from Noetik.
(4:09) The complexity of cancer: Why "curing cancer" is a misleading concept and the need for new, multimodal data.
(8:24) Identifying therapeutically relevant cancer subtypes to improve clinical trial success rates.
(11:27) The importance of intentional, high-quality data generation in AI biotech.
(17:09) Lessons learned from Recursion Pharmaceuticals regarding batch effects and data design.
(20:14) Introduction to Noetik's core data modalities: Pathology (H&E), spatial transcriptomics, and genomic alterations.
(30:15) The philosophy of self-supervised learning and avoiding bias from electronic health records.
(36:01) Translating latent space embeddings and patient clusters into actionable insights for pharma.
(41:40) Using PerturbMap and in-vivo mouse models to validate human AI predictions.
(53:38) Technical deep dive: The Tario transformer-based model and auto-regressive training objectives.
(1:00:26) The GSK partnership: Licensing OctoVC and the shift toward platform-based biotech deals.
(1:13:55) Advice for small biotech AI startups: Scaling, data conviction, and lessons from scientific history.
