TechCrunch Disrupt 2026 will host a conversation with the founder of a billion-dollar startup working on bringing extinct species back. The session focuses on how AI is being applied to biodiversity and de-extinction efforts. Passes for the event are currently on sale.
A discussion thread argues that AI capabilities in materials science and biological research deserve closer monitoring as they advance. The emphasis is on watching evaluation results in these domains rather than on any newly announced model or product. No specific findings, releases, or policy changes are tied to the item.
Researchers introduce ProMeta, a few-shot machine learning framework that forecasts how effectively PROTAC molecules degrade target proteins across different E3 ligases. PROTACs are bifunctional compounds that hijack the ubiquitin-proteasome system to eliminate disease-linked proteins long considered out of reach for conventional drugs. The method targets the scarcity of labeled data that has limited prior computational predictors for targeted degradation.
A new arXiv preprint describes a "biology-in-the-loop" framework that chooses which perturbations to test next when experimental budgets are limited. The approach amortizes the cost of adaptive decision-making so that sequential selection can be applied efficiently to CRISPR screens. The authors frame the problem as sequential experimental design for biological discovery under constrained resources.
César de la Fuente's laboratory is applying OpenAI's Codex and ChatGPT to screen genetic data from both living and extinct organisms for potential antimicrobial compounds. The goal is to surface new drug candidates that could help address infections resistant to current antibiotics. The work illustrates how AI coding and language tools are being folded into early-stage biomedical discovery.
WHY IT MATTERS ↘It shows that general-purpose coding and language models can be repurposed as cheap screening instruments in domains like drug discovery, shifting advantage to labs that can wrap them in domain-specific pipelines rather than to whoever trains the frontier model. It also widens the surface for dual-use and biosecurity scrutiny, since the same tooling that surfaces antimicrobial candidates could be pointed at other genomic targets.