Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
This work addresses the challenge of principled evaluation for molecular deep generative models, which is crucial for in silico molecule design, but it is incremental as it builds on existing methods for analysis.
The paper tackled the problem of evaluating how well deep generative models encode molecular features by proposing a method that correlates latent space metrics with topological data analysis metrics, showing that 3D topology information is consistently encoded in a VAE trained on SMILES strings.
Deep generative models are increasingly becoming integral parts of the in silico molecule design pipeline and have dual goals of learning the chemical and structural features that render candidate molecules viable while also being flexible enough to generate novel designs. Specifically, Variational Auto Encoders (VAEs) are generative models in which encoder-decoder network pairs are trained to reconstruct training data distributions in such a way that the latent space of the encoder network is smooth. Therefore, novel candidates can be found by sampling from this latent space. However, the scope of architectures and hyperparameters is vast and choosing the best combination for in silico discovery has important implications for downstream success. Therefore, it is important to develop a principled methodology for distinguishing how well a given generative model is able to learn salient molecular features. In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA). We apply our evaluation methodology to a VAE trained on SMILES strings and show that 3D topology information is consistently encoded throughout the latent space of the model.