Cover Image

TriFit: Trimodal Fusion with Protein Dynamics for Mutation Fitness Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Cho, Seungik
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Biomolecules Quantitative Methods
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MicroFuse: Protein-to-Genome Expert Fusion for Microbial Operon Reasoning
by: Cho, Seungik
Published: (2026)

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
by: Weitzman, Ruben, et al.
Published: (2025)

Improving Protein Optimization with Smoothed Fitness Landscapes
by: Kirjner, Andrew, et al.
Published: (2023)

Evolutionary Profiles for Protein Fitness Prediction
by: Fan, Jigang, et al.
Published: (2025)

Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
by: Lee, Minji, et al.
Published: (2024)

Protein-Mamba: Biological Mamba Models for Protein Function Prediction
by: Xu, Bohao, et al.
Published: (2024)

Protein Fold Classification at Scale: Benchmarking and Pretraining
by: Chen, Dexiong, et al.
Published: (2026)

Endowing Protein Language Models with Structural Knowledge
by: Chen, Dexiong, et al.
Published: (2024)

Conditioning Protein Generation via Hopfield Pattern Multiplicity
by: Varner, Jeffrey D.
Published: (2026)

Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
by: Lu, Jiarui, et al.
Published: (2024)

ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution
by: Cheng, Lixue, et al.
Published: (2022)

Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
by: Liu, Xiaokun, et al.
Published: (2025)

Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
by: Chatzianastasis, Michail, et al.
Published: (2024)

Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
by: Lu, Jiarui, et al.
Published: (2023)

Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
by: Sun, Rui, et al.
Published: (2024)

DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
by: Schwarz, Kyriakos, et al.
Published: (2022)

VARIANT: Web Server for Decoding and Analyzing Viral Mutations at Genome and Protein Levels
by: Wang, Rui, et al.
Published: (2026)

Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation
by: Wang, Zhiwei, et al.
Published: (2024)

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics
by: Liao, Danqi, et al.
Published: (2025)

A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
by: Liu, Shengchao, et al.
Published: (2024)

ProteinBench: A Holistic Evaluation of Protein Foundation Models
by: Ye, Fei, et al.
Published: (2024)

General Protein Pretraining or Domain-Specific Designs? Benchmarking Protein Modeling on Realistic Applications
by: Yan, Shuo, et al.
Published: (2025)

FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
by: Meng, Zhaohan, et al.
Published: (2024)

Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design
by: Crouzet, Simon J.
Published: (2026)

Differentiable Folding for Nearest Neighbor Model Optimization
by: Krueger, Ryan K., et al.
Published: (2025)

A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation
by: Wang, Yongkang, et al.
Published: (2023)

NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models
by: Sakano, Koh, et al.
Published: (2024)

Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
by: Chadi, Mohamed-Amine, et al.
Published: (2023)

Accurate RNA 3D structure prediction using a language model-based deep learning approach
by: Shen, Tao, et al.
Published: (2022)

Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
by: Zhang, Jiayang, et al.
Published: (2025)

ImmunoAI: Accelerated Antibody Discovery Using Gradient-Boosted Machine Learning with Thermodynamic-Hydrodynamic Descriptors and 3D Geometric Interface Topology
by: Shivakumar, Shawnak, et al.
Published: (2025)

Improving Antibody Design with Force-Guided Sampling in Diffusion Models
by: Kulytė, Paulina, et al.
Published: (2024)

Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
by: Abeer, A N M Nafiz, et al.
Published: (2024)

Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
by: Ünlü, Atabey, et al.
Published: (2023)

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
by: Gu, Siyi, et al.
Published: (2024)

Exploring zero-shot structure-based protein fitness prediction
by: Sharma, Arnav, et al.
Published: (2025)

IgCraft: A versatile sequence generation framework for antibody discovery and engineering
by: Greenig, Matthew, et al.
Published: (2025)

Is novelty predictable?
by: Fannjiang, Clara, et al.
Published: (2023)

Drug Repurposing for Parkinson's Disease Using Random Walk With Restart Algorithm and the Parkinson's Disease Ontology Database
by: Kankariya, Pratham, et al.
Published: (2024)

Optimizing Convolutional Neural Networks for Identifying Invasive Pollinator Apis Mellifera and Finding a Ligand drug to Protect California's Biodiversity
by: Swaroop, Arnav
Published: (2024)