Truong Son Hy

MutaStruct: Revisiting Benchmarks for Single-Site Mutation-Induced Structural Change Prediction

The paper introduces MutaStruct, a curated benchmark for evaluating mutation-induced structural changes, showing that overall structural fidelity does not guarantee accurate modeling of mutation effects.

Towards a Unified Framework for Protein Interaction Site Prediction Using Hierarchical Protein Representations

PRIME-ISP predicts interaction sites across diverse biomolecular interactions using a hierarchical structural framework, outperforming sequence-based methods and matching specialized approaches without task-specific features.

Machine learning for characterizing plant-insect interactions through electrical penetration graphic signal

This paper develops a machine learning approach to automate EPG signal segmentation and classification, achieving high accuracy in identifying insect feeding behaviors.

LANTERN: Leveraging Large Language Models and Transformers for Enhanced Molecular Interactions

LANTERN improves molecular interaction prediction by integrating large language models and transformers, achieving state-of-the-art results on DTI, DDI, and PPI tasks.

Generative AI-assisted Virtual Screening Pipeline for Generalizable and Efficient Drug Repurposing

This paper introduces a Generative AI-assisted Virtual Screening Pipeline for drug repurposing that enables blind screening of protein targets without prior structural knowledge.

DiscoEPG: A Python package for characterization of insect electrical penetration graph (EPG) signals

DiscoEPG is a Python package that automatically annotates insect EPG signals with improved accuracy using machine learning, offering tools for analysis and visualization.

LightMed: A Light-weight and Robust FFT-Based Model for Adversarially Resilient Medical Image Segmentation

LightMed improves medical image segmentation by using FFT-based processing to enhance robustness against noise and adversarial attacks.

Protein Design by Directed Evolution Guided by Large Language Models

This paper introduces a machine learning-guided directed evolution framework using large language models to improve protein design efficiency and effectiveness.

VieMedKG: Knowledge Graph and Benchmark for Traditional Vietnamese Medicine

This paper constructs the first knowledge graph for Traditional Vietnamese Medicine (TVM) by adapting existing Traditional Chinese Medicine (TCM) resources and develops a benchmark for knowledge graph-based question-answering.

Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models

This study introduces ProteinReDiff, a diffusion-based model for redesigning ligand-binding proteins using sequence and ligand SMILES inputs, showing effectiveness in affinity and structural preservation.

Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design

This paper introduces LDE, a variational autoencoder-based method for protein sequence design that improves efficiency by prioritizing high-fitness mutants in latent space, outperforming baselines on eight tasks.

Multimodal Pretraining for Unsupervised Protein Representation Learning

This paper introduces a symmetry-preserving multimodal pretraining framework for proteins, achieving competitive results in multiple tasks using a PyTorch implementation.