@inproceedings{
izdebski2023de,
title={De Novo Drug Design with Joint Transformers},
author={Adam Izdebski and Ewelina Weglarz-Tomczak and Ewa Szczurek and Jakub Tomczak},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=2JkSb52D1n}
}
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 show that training the model with a penalized log-likelihood objective results in state-of-the-art performance in molecule generation, while decreasing the prediction error on newly sampled molecules, as compared to a fine-tuned decoder-only Transformer, by 42%. Finally, we propose a probabilistic black-box optimization algorithm that employs Joint Transformer to generate novel molecules with improved target properties and outperform other SMILES-based optimization methods in de novo drug design.
De Novo Drug Design with Joint Transformers
Adam Izdebski Ewelina Weglarz-Tomczak Ewa Szczurek Jakub Tomczak