AI in chemistry and beyond: Machine learning for reactivity using expert descriptors and mechanistic information

Deep learning based on string or graph representations of molecules has shown great progress in the last few years. Important applications include, for example, synthesis prediction, protein structure prediction and machine learning potentials. All of these applications benefit from an abundance of well-curated datasets on the order of at least hundreds of thousands of points. For many applications in chemistry, datasets are much smaller, on the order of tens or hundreds of datapoints. Machine learning with classical methods has here been the gold standard, based on expert-picked descriptors. These descriptors are mostly problem-specific and often calculated with quantum-chemical software. During the last few years, we have developed the open-source Morfeus Python package for calculating descriptors, mainly related to catalysis and reactivity. Morfeus was for example used to calculate descriptors for the Kraken database of phosphine ligand properties.While most reactivity models include only information on reactants and/or products, increased accuracy can be obtained by including information from high-energy intermediates and transitions states along the reaction path. We will highlight our work on using mechanistic information to predict activation energies and selectivities for the nucleophilic aromatic substitution reaction, representing ~9% of all reactions carried out in the pharmaceutical industry.Although including mechanistic information can improve model performance, obtaining the required high-energy structures is time-consuming and non-robust. We have therefore worked on robuster and faster methods based on force fields in the Polanyi package. Using Polanyi, we recently created the first reactivity benchmark task for generative models in the Tartarus suite. Tartarus allows comparison on which are more chemically realistic and challenging than conventional tasks such as logP or QED optimization.