Lilly offers AI discovery models to biotech partners
Eli Lilly is offering AI-powered drug discovery models developed at a cost of around $1 billion to biotech companies for free, as long as they provide 'training data' to refine them.
The new launch – called Lilly TuneLab – is a machine learning (ML) platform that the company says has been "trained on years of […] research data" and represents "one of the industry's most valuable datasets used to train an AI system available."
It is an addition to the Catalyze360 suite of offerings that Lilly makes available to its early-stage biotech partners to help them bring innovative new projects through development more quickly, along with access to lab facilities and scientific mentorship, R&D software, and financial investments.
AI has emerged as a transformative force in the life sciences industry thanks to its ability to process large-scale datasets, uncover patterns, and generate predictions, and it is already being used to find drug targets, design new drugs, and repurpose existing therapies, among other applications.
"Lilly has spent decades building comprehensive datasets for drug discovery. Today, we're sharing the intelligence gained from that investment to help lift the tide of biotechnology research," said Daniel Skovronsky, Lilly's chief scientific officer.
"Lilly TuneLab was created to be an equaliser so that smaller companies can access some of the same AI capabilities used every day by Lilly scientists," he added. "By opening up access, we hope to accelerate the creation of new medicines for patients who need them."
Some companies have already said they are partnering with Lilly for TuneLab, including two South San Francisco biotechs.
Circle Pharma will use it to develop new macrocycle therapeutics for hard-to-treat cancers, while Insitro – a specialist in AI-powered drug discovery itself – will work with Lilly on advanced ML models that can accurately predict key pharmacological properties of small molecules.
"The rapid design of safe and effective small molecules has long been a holy grail in drug discovery, but has been stymied by the unpredictability of key pharmacological properties, such as a molecule's behaviour in vivo," said Insitro founder and CEO Daphne Koller.
"AI can address this challenge, but only with robust, coherent, and consistently collected data on advanced molecules, data that [is] very rarely found," she added.
"That is why we are especially excited to again partner with Lilly in bringing our ML capabilities to their unique dataset, so we can build best-in-class predictive models for small molecule properties, and bring the benefits of delivering better drugs faster to the patients who are waiting."
