SmilesFormer: Language Model for Molecular Design

The objective of drug discovery is to find novel compounds with desirable chemical properties. Generative models have been utilized to sample molecules at the intersection of multiple property constraints. In this paper we pose molecular design as a language modeling problem where the model implicitly learns the vocabulary and composition of valid molecules, hence it is able to generate new molecules of interest. We present SmilesFormer, a Transformer-based model which is able to encode molecules, molecule fragments, and fragment compositions as latent variables, which are in turn decoded to stochastically generate novel molecules. This is achieved by fragmenting the molecules into smaller combinatorial groups, then learning the mapping between the input fragments and valid SMILES sequences. The model is able to optimize molecular properties through a stochastic latent space traversal technique. This technique systematically searches the encoded latent space to find latent vectors that are able to produce molecules to meet the multi-property objective. The model was validated through various de novo molecular design tasks, achieving state-of-the-art performances when compared to previous methods. Furthermore, we used the proposed method to demonstrate a drug rediscovery pipeline for Donepezil, a known Acetylcholinesterase Inhibitor.