Generating stable molecules using imitation and reinforcement learning
This work addresses the challenge of generating stable molecules for chemical discovery, which is incremental by building on existing methods with 3D data.
The authors tackled the problem of generating stable molecules by incorporating 3D information, which is often ignored in graph-based methods, using a reinforcement learning approach with imitation learning for sample efficiency. They achieved results such as correctly identifying low-energy molecules and producing novel isomers not in the training set, with application to larger molecules.
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these methods often rely on a graph representation, ignoring 3D information necessary for determining the stability of the molecules. We propose a reinforcement learning approach for generating molecules in cartesian coordinates allowing for quantum chemical prediction of the stability. To improve sample-efficiency we learn basic chemical rules from imitation learning on the GDB-11 database to create an initial model applicable for all stoichiometries. We then deploy multiple copies of the model conditioned on a specific stoichiometry in a reinforcement learning setting. The models correctly identify low energy molecules in the database and produce novel isomers not found in the training set. Finally, we apply the model to larger molecules to show how reinforcement learning further refines the imitation learning model in domains far from the training data.