Artificial intelligence accelerates drug development
Drug development requires the management of complex chemical processes. A new study seeks efficiency for development through machine learning.
Orion Pharma and the University of Helsinki are launching a study that combines chemistry with AI. “The goal is to optimise pharmaceutical development so that new, effective and safe pharmaceutical ingredients can be made available to patients more rapidly,” says Julius Sipilä, Head of Medicinal Chemistry at Orion’s R&D.
The development of chemical processes with the help of AI is a relatively new phenomenon globally. “Over the past year, interesting reports have been published on projects in which machine learning has been applied to optimising synthesis reactions on a large scale for the first time,” says Sipilä.
The joint project between the University of Helsinki and Orion Pharma combines the latest chemical methods with machine learning – that is, AI – in research into new pharmaceutical ingredients. The study is the first of its kind in Finland, and has been granted EUR 220,000 in funding by the Finnish Research Impact Foundation.
The study seeks to optimise pharmaceutical syntheses and identify more efficient ways to prepare new pharmaceutical ingredients. At the same time, the goal is to pave the way for a new type of education for future chemists, so they will be better able to combine new methods in machine learning and synthesis technology.
Higher speed and accuracy through computing power
AI will be used to support chemists, because their task is complex and demanding. “When we are developing new pharmaceutical ingredients, we have to synthesise a specific compound for the first time ever in the whole world. To begin with, the synthesis route needs to be designed. This consists of multiple reactions, and each of these must be successful. Precursors, reagents, catalysts, solvents, temperatures and volumes must be selected for each reaction. There are many variables, and each reaction involves millions of options,” Sipilä explains.
The study uses all the reaction data that has been accumulated during previous projects. “We are trying to use data, both from our own projects and publicly available reaction databases, as effectively as possible and process it for machine learning. Then we will use the data to aid experimental chemistry.”
Sipilä says that optimisation cannot be carried out until the first successful reactions have been conducted and the new ingredient is ready for the first tests. When the effectiveness of a new pharmaceutical ingredient is being tested, the quantity required grows rapidly.
“The quantity needed increases quickly, by thousands of times. This is when the synthesis route must be optimised as efficiently as possible and machine learning is needed. The first reactions provide data to determine what is working and what is not. Then the machine suggests which optimisation experiment should be conducted next. Compared with chemists working without such a tool, we can proceed more quickly to sufficiently effective reactions by using AI.”
