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arXiv · 2310.02066

De Novo Drug Design with Joint Transformers

Adam Izdebski·Ewelina Weglarz-Tomczak·Ewa Szczurek·Jakub M. Tomczak
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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.

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Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek, Jakub M. Tomczak. 2023-10-03. De Novo Drug Design with Joint Transformers. https://arxiv.org/abs/2310.02066

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