Conversational Chemistry: A Novel Approach to Chemical Search and Property Prediction

Abstract

We have developed an approach to train a chemical property prediction model using both English and the SELFIES chemical language describing the structure of small, drug-like molecules. This model generates chemical embedding vectors, which we then use to train classification models. Our straightforward softmax classification model surpasses the commonly-used message passing neural network architecture in certain chemical property prediction tasks. Moreover, these chemical embedding vectors can be employed in other applications, such as building a chemical search engine that enables users to find new drugs with natural language queries (e.g., "low toxicity blood brain barrier permeable drug that inhibits HIV replication").