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.
@article{arxiv.2310.02066,
title = {De Novo Drug Design with Joint Transformers},
author = {Adam Izdebski and Ewelina Weglarz-Tomczak and Ewa Szczurek and Jakub M. Tomczak},
journal= {arXiv preprint arXiv:2310.02066},
year = {2023}
}
Comments
Accepted to NeurIPS 2023 Generative AI and Biology Workshop