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 was the primary goal of the AIDD proposal. Research WPs included sixteen topics selected to cover the key innovative directions in machine learning in chemistry. The fellows employed were supervised by academics with excellent complementary expertise, that have contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies leading development of new medicines and public health. All methods developed within AIDD can be used individually, but are also integrated in the "One Chemistry" model, which can predict outcomes ranging from different properties to molecule generation and synthesis. Training on various modalities allows the model to understand how to intertwine chemistry and biology to develop a new drug making its design robust. All partners agreed to make the software developed within the AIDD project open-source, and has achieved the broadest possible dissemination of the results both to the academy and industry, including SMEs. The network provided comprehensive, structured training to its fellows through a well-elaborated curriculum, online courses, and six Schools.
This project was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956832, and it is Horizon 2020 Marie Skłodowska-Curie Innovative Training Network - European Industrial Doctorate.
We are on social networks: Bluesky, LinkedIn, Facebook (previously also on twitter). See also project publications at Google Scholar as well as publications in AIDD special issue published by J. Cheminformatics. Two articles are currently listed as "highly cited" according to the Web of Knowledge. Public versions of tools developed within AIDD can be found on our GitHub.