Pharmaceutical companies have traditionally kept their data close to the vest because collaboration’s side effects may include compromising intellectual property and losing the edge over competitors. But sharing data has major perks: The more data a pharma company has at its disposal, the better equipped its researchers are to quickly identify and develop promising new drugs. This can ultimately improve drug candidate success rates and reduce treatment costs.
Bringing a drug to market takes on average 13 years and close to $2 billion, said Hugo Ceulemans, project leader of MELLODDY — a new drug-discovery consortium that hopes to eliminate the tradeoff between data sharing and security. The project will use cloud-based NVIDIA GPUs and a distributed approach known as federated learning to train AI models on data from multiple pharmaceutical companies while preserving IP.
