Optimising biological activity and ADME properties, while minimising toxicity, are objectives when developing new compounds. Advanced machine learning methods are indispensable to this process. The project will develop and benchmark representation learning approaches, addressing their accuracy and explainability, using public and in-house data for endpoints ranging from chemical reactions to toxicity. The program will be done with the target users: large companies, regulatory agencies and SMEs.

The dramatic increase in using of Artificial Intelligence (AI) and traditional machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro biotech, and other chemical companies. However, the application of AI in these fields is not straightforward and requires excellent knowledge of chemistry. Thus, there is a strong need to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry, which is the prime goal of the AIDD proposal.

Our vision is that every patient should have access to​ the most up-to-date individualised treatments and to innovative therapies.​ By strengthening shared decision-making based on dynamic computer-interpretable guidelines (CIGs), innovative broad data access and AI-driven technology and tools, we envision revolutionizing oncology care in Europe.OPTIMA is funded through the IMI2 Joint Undertaking and is listed under grant agreement No. 101034347. IMI2 receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).The views communicated within are those of OPTIMA. Neither the IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

The vision of the ONTOX consortium is to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment.

A central repository of digital pathology slides to boost the development of artificial intelligence

MELLODDY aims to leverage the world’s largest collection of small molecules with known biochemical or cellular activity to enable more accurate predictive models and increase efficiencies in drug discovery.This project aims to enhance predictive Machine Learning models on decentralised data of 10 pharmaceutical companies, without exposing proprietary information.

Discovery of gene expression signatures for prediction of progression-free survival in colorectal cancer.

Modeling of stationary and dynamic processes in high-dimensional biological systems.

Nurtured Development - controlling the stable microclimate of a incubator