The content discusses the development of DiMA, a diffusion-based model for generating protein sequences using language model embeddings. It explores the importance of unconditional generation in protein design and highlights the impact of design choices on performance. The study evaluates the quality, diversity, distribution similarity, and biological relevance of generated sequences across various metrics and modalities.
The paper emphasizes the significance of understanding protein universe complexities and introduces DiMA as a pivotal domain exploration tool. It showcases how this approach advances protein design by providing high-quality sequence generation capabilities. The content also delves into related work on diffusion generative models and their applications in text domains.
Furthermore, it details the training process, noise schedules, self-conditioning techniques, decoder architecture, length sampling methods, and model modifications for effective operation within the protein-related data context. The experiments conducted on two datasets demonstrate DiMA's superior performance compared to baseline architectures in terms of quality, diversity, distribution similarity, and biological relevance.
Overall, the study presents a comprehensive analysis of DiMA's capabilities in generating diverse variants of natural-like proteins through continuous diffusion modeling with language model embeddings.
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arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Viacheslav M... : arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03726.pdfDaha Derin Sorular