GPepT is a cutting-edge language model designed to understand and generate sequences in the specialized domain of peptides and peptidomimetics. It serves as a powerful tool for de novo protein design and engineering. As demonstrated in our research, the incorporation of peptidomimetics significantly broadens the chemical space accessible through generated sequences, enabling innovative approaches to peptide-based therapeutics. GPepT builds upon the GPT-2 Transformer architecture, comprising 36 layers and a model dimensionality of 1280, with a total of 738 million parameters. This decoder-only model has been pre-trained on a curated dataset of peptides and peptidomimetics mined from bioactivity-labeled chemical formulas in ChEMBL. To leverage GPepT’s pre-trained weights, input molecules must be converted into a standardized sequence-like representation of peptidomimetics using Monomerizer. Detailed insights into the training process and datasets are provided in our accompanying publication. Unlike traditional protein design models, GPepT is trained in a self-supervised manner, using raw sequence data without explicit annotation. This design enables the model to generalize across…
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Генерация текста
Задача: Генерация текста
Автор: Playingyoyo
Теги: gpt2
Лайков: 4 | Загрузок: 409
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
