Under the hood: 5 practical lessons from developing Large Language Model Applications for Drug Discovery

The biotech industry has been quick to explore the potential of Large Language Models, yet practical insights remain scarce. In a field that is both art and science, experience is key. Here we share our lessons learned from building LLM applications in drug discovery.

Read More

From the Depths of Literature: How Large Language Models Excavate Crucial Information to Scale Drug Discovery

In drug discovery, excavating the right information about potential drug targets and molecules from the depths of the scientific literature is key to success in biotech. Large Language Models will change the nature of this game. Here is how, in three concrete examples.

Read More

No-nonsense: How ChatGPT-Technology helps Biotechs find better Drug Targets faster

Large Language Models are poised to become an indispensable tool for biotechs looking to find their ideal drug targets. Evidence-grounded LLMs can sift through millions of publications, finding highly specific pieces of evidence in seconds, unlocking overlooked drug target opportunities.

Read More

Data management for early-stage biotechs: how to get started on the right path

Data management at a biotech needs to work for everyone – from CEO’s courting investors to lab scientists designing the next experiment. Get it wrong and you’ll either need to put up with your system’s foibles or go through a painful migration to a new system.

Read More

Into the unknown: biotech’s quest to target the 'undruggable' proteome

Novel therapeutic modalities, such as PROTACs, allow access to formerly undruggable parts of the proteome, shifting the frontiers of drug discovery. Artificial intelligence-based target scoring algorithms can help biotechs prioritize disease-relevant proteins as modality tailored drug targets.

Read More