Open-Source Molecular Processing Pipeline for Generating Molecules

The discovery of new molecules and materials is crucial for addressing challenges in chemistry, such as treating diseases and tackling climate change [Liu et al., 2023, Sanchez and Aspuru-Guzik, 2018]. Traditional methods rely on human expertise and are time-consuming and costly, limiting the exploration of the vast chemical space [Polishchuk et al., 2013]. Generative models offer a promising solution using deep learning to design molecules based on desired properties, rapidly identifying diverse and optimized molecules for specific applications. These models vary in their approaches and have seen rapid development, with benchmarks now in place to evaluate their performance in terms of distribution learning and chemical diversity. Although these models are publicly available, practitioners require extensive Python and machine learning knowledge to reap their benefits. Thus, we introduce open-source molecular generative model infrastructure into DeepChem Ramsundar et al. [2019], a widely used open-source library for molecular machine learning.