Cover Image

Machine learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kumar, Gaurav, Ardekani, Arezoo M.
Format:	Preprint
Published:	2024
Subjects:	Biomolecules
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Selective profiling of non-canonical nucleic acid structures via size-discriminative supramolecular probes
by: Shi, Runyu, et al.
Published: (2025)

DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics
by: Mondal, Somnath, et al.
Published: (2025)

Machine learning predictions from unpredictable chaos
by: Jiang, Jian, et al.
Published: (2025)

PDCNet: a benchmark and general deep learning framework for activity prediction of peptide-drug conjugates
by: Liu, Yun, et al.
Published: (2025)

Machine learning and natural language processing models to predict the extent of food processing
by: Arora, Nalin, et al.
Published: (2024)

ViraHinter: a dual-modal artificial intelligence framework for predicting virus-host interactions
by: Bai, Weiqiang, et al.
Published: (2026)

OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
by: Nie, Zhiwei, et al.
Published: (2025)

Machine learning for RNA-targeting drug design
by: Karroucha, Wissam, et al.
Published: (2025)

Deep learning assisted SERS detection of prolines and hydroxylated prolines using nitrilotriacetic acid functionalized gold nanopillars
by: Zhang, Yuan, et al.
Published: (2024)

Machine learning-assisted search for novel coagulants: when machine learning can be efficient even if data availability is low
by: Rovenchak, Andrij, et al.
Published: (2024)

Application of machine learning to predict food processing level using Open Food Facts
by: Arora, Nalin, et al.
Published: (2025)

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins
by: von Bülow, Sören, et al.
Published: (2024)

AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy
by: Wang, Tianyue, et al.
Published: (2025)

CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
by: Feng, Hongsong, et al.
Published: (2025)

CBTOPE2: An improved method for predicting of conformational B-cell epitopes in an antigen from its primary sequence
by: Pandey, Anupma, et al.
Published: (2025)

Breaking the picomolar barrier in lateral flow assays using Bright-Dtech___ 614 -- Europium nanoparticles for enhanced sensitivity
by: Lajoux, Juliette, et al.
Published: (2025)

Mayer-homology learning prediction of protein-ligand binding affinities
by: Feng, Hongsong, et al.
Published: (2024)

T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction
by: Ibraheem, Abdulrahman, et al.
Published: (2024)

edible polysaccharides as stabilizers and carriers for the delivery of phenolic compounds and pigments in food formulations
by: de Oliveira, Liliane Siqueira, et al.
Published: (2025)

Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics
by: Athènes, Gabriel, et al.
Published: (2025)

Aptamer-protein interaction prediction model based on transformer
by: Yan, Zhichao, et al.
Published: (2025)

Fold-switching proteins push the boundaries of conformational ensemble prediction
by: Lee, Myeongsang, et al.
Published: (2026)

Persistent local Laplacian prediction of protein-ligand binding affinities
by: Liu, Jian, et al.
Published: (2026)

Kirigami: large convolutional kernels improve deep learning-based RNA secondary structure prediction
by: Harary, Marc, et al.
Published: (2024)

GraphRT: A graph-based deep learning model for predicting the retention time of peptides
by: Drvodelic, Mark, et al.
Published: (2024)

AlphaFold2 for protein structure prediction: Best practices and critical analyses
by: Radjasandirane, Ragousandirane, et al.
Published: (2024)

Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications
by: Yang, Shu, et al.
Published: (2025)

Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction
by: Banerjee, Sharmi, et al.
Published: (2025)

Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture
by: Abdel-Rehim, Abbi, et al.
Published: (2024)

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor
by: Ye, Jing, et al.
Published: (2024)

StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides
by: Wang, Zhe, et al.
Published: (2024)

Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
by: Chakravarty, Devlina, et al.
Published: (2024)

State-aware protein-ligand complex prediction using AlphaFold3 with purified sequences
by: Xing, Enming, et al.
Published: (2025)

Multiscale differential geometry learning for protein flexibility analysis
by: Feng, Hongsong, et al.
Published: (2024)

Best Practices for Machine Learning-Assisted Protein Engineering
by: Herrera-Rocha, Fabio, et al.
Published: (2025)

Compact assessment of molecular surface complementarities enhances neural network-aided prediction of key binding residues
by: Grassmann, Greta, et al.
Published: (2024)

DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
by: Lupo, Umberto, et al.
Published: (2024)

Towards deep learning sequence-structure co-generation for protein design
by: Wang, Chentong, et al.
Published: (2024)

Reversible molecular simulation for training classical and machine learning force fields
by: Greener, Joe G
Published: (2024)

Discrimination vs. Generation: The Machine Learning Dichotomy for Dopaminergic Hit Discovery
by: Sobodu, Temitope, et al.
Published: (2024)