Annealed fractional Lévy–Itō diffusion models for protein generation

Par Conseil national de recherches du Canada

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DOI	Trouver le DOI : https://doi.org/10.1016/j.csbj.2024.04.009
Auteur	Rechercher : Paquet, Eric¹Identifiant ORCID : https://orcid.org/0000-0001-6515-2556; Rechercher : Soleymani, Farzan; Rechercher : Viktor, Herna Lydia; Rechercher : Michalowski, Wojtek Identifiant ORCID : https://orcid.org/0000-0002-9198-6439
Affiliation	Conseil national de recherches du Canada. Technologies numériques
Bailleur de fonds	Rechercher : National Research Council Canada
Format	Texte, Article
Sujet	diffusion; fractional; generative model; Lévy–Itō; noising; protein; stable distribution; stochastic differential equation; score
Résumé	Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40,000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.
Date de publication	2024-04-17
Maison d’édition	Elsevier
Licence	Creative Commons Attribution - Pas d'utilisation commerciale - Pas de modification 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/deed.fr
Dans	Computational and Structural Biotechnology Journal 23, nº C (17 avril 2024) : 1641–1653.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
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Identificateur de l’enregistrement	c1cf08b1-a648-4900-b6b5-06cec6025624
Enregistrement créé	2024-04-22
Enregistrement modifié	2024-04-22

Date de modification :: 2024-06-30