1. De Novo Drug Design with Joint Transformers
- Author
-
Izdebski, Adam, Weglarz-Tomczak, Ewelina, Szczurek, Ewa, and Tomczak, Jakub M.
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models. To address this, we propose Joint Transformer that combines a Transformer decoder, Transformer encoder, and a predictor in a joint generative model with shared weights. We formulate a probabilistic black-box optimization algorithm that employs Joint Transformer to generate novel molecules with improved target properties and outperforms other SMILES-based optimization methods in de novo drug design., Comment: Accepted to NeurIPS 2023 Generative AI and Biology Workshop
- Published
- 2023