Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines

De novo design of small molecules using variational and conditional variational autoencoders

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Jordi Vitrià i Marca[en] Chemical space is estimated to contain over 10 60 small synthetically feasible molecules and so far only a fraction of the space has been explored. Experimental techniques are time-consuming and expensive so computational methods, such as machine learning, are needed for efficient exploration. Here we looked at generative models, more specifically variational autoencoder (VAE) and conditional variational autoencoder (CVAE), used for designing new molecules. In the first part, we evaluated already written VAE and in the second part, we upgraded it to the CVAE. For the conditional vectors in CVAE we used B4 Signatures generated from Chemical Checker describing molecular properties. Both models performed well, however, CVAE showed many advantages