Generating New Molecules with Graph Grammar.
Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. However, deep learning requires extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world.
Now, a new paper
from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data. To do this, they treat the formation of atoms and chemical bonds as a graph and develop a graph grammar — a linguistics analogy of systems and structures for word ordering — that contains a sequence of rules for building molecules, such as monomers and polymers. Using the grammar and production rules that were inferred from the training set, the model can not only reverse engineer its examples, but can create new compounds in a systematic and data-efficient way.#News#MachineLearning