Turing AI Fellowship: Machine Learning for Molecular Design

Many existing challenges, from personalized health care to energy production and storage, require the design and manufacture of new molecules. However, identifying new molecules with desired properties is difficult and time-consuming. We aim at accelerating this process by exploiting advances in data availability, computing power, and AI. We will create generative models of molecules that operate by placing atoms in 3D space. These are more realistic and can produce better predictions than alternative approaches based on molecular graphs. Our models will guarantee that the generated molecules are synthetically accessible upfront. This will be achieved by mirroring realistic real-world processes for molecule generation where reactants are first selected, and then combined into more complex molecules via chemical reactions. Additionally, our methods will be reliable, by accounting for uncertainty in parameter estimation, and data-efficient, by jointly learning from different data sources. Our contributions will have a broad impact on materials science, leading to more effective flow batteries, solar cell components, and organic light-emitting diodes. We will also contribute to accelerate the drug discovery process, leading to more economic and effective drugs that can significantly improve the health and lifestyle of millions.