Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Fuente:
arXiv
Guardado en:
| Autores principales: | Rodrigues, Ana F., Ferraz, Lucas, Balbi, Laura, Cotovio, Pedro Giesteira, Pesquita, Catia |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
por: Ferraz, Lucas, et al.
Publicado: (2026)
por: Ferraz, Lucas, et al.
Publicado: (2026)
Continued domain-specific pre-training of protein language models for pMHC-I binding prediction
por: Mares, Sergio E., et al.
Publicado: (2025)
por: Mares, Sergio E., et al.
Publicado: (2025)
Reliable algorithm selection for machine learning-guided design
por: Fannjiang, Clara, et al.
Publicado: (2025)
por: Fannjiang, Clara, et al.
Publicado: (2025)
Deep learning-guided evolutionary optimization for protein design
por: Hartman, Erik, et al.
Publicado: (2026)
por: Hartman, Erik, et al.
Publicado: (2026)
ECKO: Explainable Clinical Knowledge for Oncology
por: Silva, Marta Contreiras, et al.
Publicado: (2025)
por: Silva, Marta Contreiras, et al.
Publicado: (2025)
Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction
por: Michalewicz, Kevin, et al.
Publicado: (2025)
por: Michalewicz, Kevin, et al.
Publicado: (2025)
Opportunities and challenges in protein structure prediction
por: Wenkai Wang, et al.
Publicado: (2026)
por: Wenkai Wang, et al.
Publicado: (2026)
Exploring zero-shot structure-based protein fitness prediction
por: Sharma, Arnav, et al.
Publicado: (2025)
por: Sharma, Arnav, et al.
Publicado: (2025)
Using a quantitative assessment of propulsion biomechanics in wheelchair racing to guide the design of personalized gloves: a case study
por: Chénier, Félix, et al.
Publicado: (2023)
por: Chénier, Félix, et al.
Publicado: (2023)
Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation
por: Wee, JunJie, et al.
Publicado: (2024)
por: Wee, JunJie, et al.
Publicado: (2024)
PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddings
por: Xu, Ziyang, et al.
Publicado: (2023)
por: Xu, Ziyang, et al.
Publicado: (2023)
Why risk matters for protein binder design
por: Cotet, Tudor-Stefan, et al.
Publicado: (2025)
por: Cotet, Tudor-Stefan, et al.
Publicado: (2025)
Pre-trained protein language model for codon optimization
por: Pathak, Shashank, et al.
Publicado: (2024)
por: Pathak, Shashank, et al.
Publicado: (2024)
Plant and insect proteins support optimal bone growth and development; Evidences from a pre-clinical model
por: Becker, Gal, et al.
Publicado: (2024)
por: Becker, Gal, et al.
Publicado: (2024)
Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation
por: Trettner, Kylie J., et al.
Publicado: (2023)
por: Trettner, Kylie J., et al.
Publicado: (2023)
Controllable protein design with particle-based Feynman-Kac steering
por: Hartman, Erik, et al.
Publicado: (2025)
por: Hartman, Erik, et al.
Publicado: (2025)
Improved prediction of ligand-protein binding affinities by meta-modeling
por: Lee, Ho-Joon, et al.
Publicado: (2023)
por: Lee, Ho-Joon, et al.
Publicado: (2023)
Edge-aware GAT-based protein binding site prediction
por: Yang, Weisen, et al.
Publicado: (2026)
por: Yang, Weisen, et al.
Publicado: (2026)
bio2Byte Tools deployment as a Python package and Galaxy tool to predict protein biophysical properties
por: Gavalda-Garcia, Jose, et al.
Publicado: (2024)
por: Gavalda-Garcia, Jose, et al.
Publicado: (2024)
Beware of so-called 'good' correlations: a statistical reality check on individual mRNA-protein predictions
por: Gosselin, Romain-Daniel
Publicado: (2025)
por: Gosselin, Romain-Daniel
Publicado: (2025)
The CAST package for training and assessment of spatial prediction models in R
por: Meyer, Hanna, et al.
Publicado: (2024)
por: Meyer, Hanna, et al.
Publicado: (2024)
ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution
por: Chervov, Alexander, et al.
Publicado: (2024)
por: Chervov, Alexander, et al.
Publicado: (2024)
AI-predicted protein deformation encodes energy landscape
por: Mcbride, John M, et al.
Publicado: (2023)
por: Mcbride, John M, et al.
Publicado: (2023)
Combining oligo pools and Golden Gate cloning to create protein variant libraries or guide RNA libraries for CRISPR applications
por: Valero, Alicia Maciá, et al.
Publicado: (2024)
por: Valero, Alicia Maciá, et al.
Publicado: (2024)
Generative modeling of protein ensembles guided by crystallographic electron densities
por: Maddipatla, Sai Advaith, et al.
Publicado: (2024)
por: Maddipatla, Sai Advaith, et al.
Publicado: (2024)
T cell receptor binding prediction: A machine learning revolution
por: Weber, Anna, et al.
Publicado: (2023)
por: Weber, Anna, et al.
Publicado: (2023)
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
por: Li, Wen-ran, et al.
Publicado: (2025)
por: Li, Wen-ran, et al.
Publicado: (2025)
Protein design and RNA design: Perspectives
por: Xi Chen, et al.
Publicado: (2025)
por: Xi Chen, et al.
Publicado: (2025)
A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing
por: Yang, Zhijian, et al.
Publicado: (2020)
por: Yang, Zhijian, et al.
Publicado: (2020)
Generative power of a protein language model trained on multiple sequence alignments
por: Sgarbossa, Damiano, et al.
Publicado: (2022)
por: Sgarbossa, Damiano, et al.
Publicado: (2022)
Zepyros: A webserver to evaluate the shape complementarity of protein-protein interfaces
por: Miotto, Mattia, et al.
Publicado: (2024)
por: Miotto, Mattia, et al.
Publicado: (2024)
Exploring accuracy and uncertainty quantification in physics-informed neural networks for inferring microbial community dynamics
por: Fontanarrosa, Pedro, et al.
Publicado: (2025)
por: Fontanarrosa, Pedro, et al.
Publicado: (2025)
Using protein blocks to build custom fragment libraries from protein structures
por: Dhingra, Surbhi, et al.
Publicado: (2020)
por: Dhingra, Surbhi, et al.
Publicado: (2020)
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
por: Mascaro, Steven, et al.
Publicado: (2025)
por: Mascaro, Steven, et al.
Publicado: (2025)
Can machine learning predict citizen-reported angler behavior?
por: Schmid, Julia S., et al.
Publicado: (2024)
por: Schmid, Julia S., et al.
Publicado: (2024)
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
por: Li, Thomas Z., et al.
Publicado: (2024)
por: Li, Thomas Z., et al.
Publicado: (2024)
Evolutionary accumulation modelling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance
por: Renz, Jessica, et al.
Publicado: (2024)
por: Renz, Jessica, et al.
Publicado: (2024)
Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops
por: Borisiak, Kevin, et al.
Publicado: (2024)
por: Borisiak, Kevin, et al.
Publicado: (2024)
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificity
por: Fang, Xing, et al.
Publicado: (2024)
por: Fang, Xing, et al.
Publicado: (2024)
MP-GCAN: a highly accurate classifier for $α$-helical membrane proteins and $β$-barrel proteins
por: Li, Kunyang, et al.
Publicado: (2025)
por: Li, Kunyang, et al.
Publicado: (2025)
Ejemplares similares
-
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
por: Ferraz, Lucas, et al.
Publicado: (2026) -
Continued domain-specific pre-training of protein language models for pMHC-I binding prediction
por: Mares, Sergio E., et al.
Publicado: (2025) -
Reliable algorithm selection for machine learning-guided design
por: Fannjiang, Clara, et al.
Publicado: (2025) -
Deep learning-guided evolutionary optimization for protein design
por: Hartman, Erik, et al.
Publicado: (2026) -
ECKO: Explainable Clinical Knowledge for Oncology
por: Silva, Marta Contreiras, et al.
Publicado: (2025)