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- W4387356211 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, a 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, as compared to the training data, outperforming other SMILES-based optimization methods in de novo drug design." @default.
- W4387356211 created "2023-10-05" @default.
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- W4387356211 date "2023-10-03" @default.
- W4387356211 modified "2023-10-06" @default.
- W4387356211 title "De Novo Drug Design with Joint Transformers" @default.
- W4387356211 doi "https://doi.org/10.48550/arxiv.2310.02066" @default.
- W4387356211 hasPublicationYear "2023" @default.
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