ChemNav: An interactive visual tool to navigate in the latent space for chemical molecules discovery

Abstract

In recent years, AI-driven drug development has emerged as a prominent research topic in computer chemistry. A key focus is the application of generative models for molecule synthesis, which create extensive virtual libraries of chemical molecules based on latent spaces. However, locating molecules with desirable properties within the vast latent spaces remains a significant challenge. Large regions of invalid samples in the latent space, called “dead zones”, can impede the exploration efficiency. The process is always time-consuming and repetitive. Therefore, we aim to propose a visualization system to help experts identify potential molecules with desirable properties as they wander in the latent space. Specifically, we conducted a literature survey about the application of generative networks in drug synthesis to summarize the tasks and followed this with expert interviews to determine their requirements. Based on the above requirements, we introduce ChemNav, an interactive visual tool for navigating latent space for desirable molecules search. ChemNav incorporates a heuristic latent space interpolation path search algorithm to enhance the efficiency of valid molecule generation, and a similar sample search algorithm to accelerate the discovery of similar molecules. Evaluations of ChemNav through two case studies, a user study, and experiments demonstrated its effectiveness in inspiring researchers to explore the latent space for chemical molecule discovery.