MolBit: De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem

Deep generative models for molecular generation have accelerated the development of de novo drug design by introducing how to generate novel molecular structures expressed in simplified molecular-input line-entry system (SMILES) or molecular graph formats. Numerous drug design studies have proposed combinations of variational autoencoder (VAE) and autoregressive generators such as recurrent neural networks (RNNs) to generate SMILES strings. However, RNN-VAE has one notorious issue, called posterior collapse, in which different latent vectors produce indistinguishable molecular distributions. In this study, we proposed a Gumbel-Softmax-based generative model, MolBit, and a genetic algorithm-based molecular property optimization method. We confirmed that the proposed model avoided the posterior collapse problem and outperformed the existing drug design models with SMILES.