De Novo Drug Design with Joint Transformers

• URL: http://arxiv.org/abs/2310.02066v3
• Date: Mon, 4 Dec 2023 08:34:48 GMT
• Title: De Novo Drug Design with Joint Transformers
• Authors: Adam Izdebski and Ewelina Weglarz-Tomczak and Ewa Szczurek and Jakub M. Tomczak
• Abstract summary: De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties. 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.
• Score: 9.339914898177186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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