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Desupervised and Red Glead Discovery receive R&D grant

Johan Evenäs and Michael Green

The two companies receive a Eurostars R&D grant to deliver AId, a novel artificial intelligence-based tool for drug discovery.

In a strategic partnership between Desupervised and Red Glead Discovery the companies have joined forces to create AI-based solutions enabling companies to reduce cost and time in small molecule drug research and development, they state.

“While our service platform increasingly attracts international biotech and large, global pharmaceutical companies, we are keen to extend and improve our service offer with AI-technology. This project enables us to contribute with our expert knowledge in small molecule drug discovery to co-develop and implement state-of-the art AI tools, empowering us to better respond to future client needs,” says Johan Evenäs, CEO Red Glead Discovery.

Combining drug discovery and AI

Desupervised will leverage Bayesian deep learning models that quantify uncertainty for more reliable prediction of novel molecular structures for drug discovery projects. Such models need less data for training and enable domain knowledge inclusion, making drug discovery easier and faster, states the companies. Currently, first-generation AI-tools that are being adopted by large pharma companies require a significant investment in both implementing and operating such tools. Beside its technical advantages, AId will be easy to implement also for smaller organizations to achieve significant improvement of workflows facilitated by AI technology, states the two companies.

Red Glead Discovery, providing integrated drug discovery services, has delivered several small molecule candidate drugs for progress to clinical trials in CNS and cancer indications to clients. Combining the partners’ expert knowledge from entirely different sectors, drug discovery and artificial intelligence (AI), the AId project aims at introducing an entirely new product to the market.

Photo of Johan Evenäs, Red Glead Discovery, and Michael Green, Desupervised