Papers Protein Language Model
“Protein Language Model” 태그가 달린 논문 119편 · 필터 해제
AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency. Engineering capsids to overcome these hurd…
Protein Language ModelReinforcement LearningProtein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such …
Protein Language ModelMinimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Existing discrete diffusion and flow-based me…
Protein Language ModelGPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery
G protein-coupled receptors (GPCRs) govern diverse physiological processes and are central to modern pharmacology. Yet discovering GPCR modulators remains challenging because receptor activation often arises from complex…
Protein Language ModelDuaDeep-SeqAffinity: Dual-Stream Deep Learning Framework for Sequence-Only Antigen-Antibody Affinity Prediction
Predicting the binding affinity between antigens and antibodies is fundamental to drug discovery and vaccine development. Traditional computational approaches often rely on experimentally determined 3D structures, which …
Protein Language ModelDrug DiscoveryHD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitful developments in protein language model…
Protein Structure PredictionProtein Language ModelSelf Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design
Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language models (PLMs) remains ad hoc. This is i…
Protein Language ModelProtein DesignExploring Protein Language Model Architecture-Induced Biases for Antibody Comprehension
Recent advances in protein language models (PLMs) have demonstrated remarkable capabilities in understanding protein sequences. However, the extent to which different model architectures capture antibody-specific biologi…
Protein Language ModelDe novo generation of functional terpene synthases using TpsGPT
Terpene synthases (TPS) are a key family of enzymes responsible for generating the diverse terpene scaffolds that underpin many natural products, including front-line anticancer drugs such as Taxol. However, de novo TPS …
Protein Language ModelProtein DesignEnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
Accurate prediction of enzyme kinetic parameters is crucial for drug discovery, metabolic engineering, and synthetic biology applications. Current computational approaches face limitations in capturing complex enzyme-sub…
Protein Language ModelContrastive LearningDrug DiscoveryDeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics
The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein…
Protein Language ModelProtein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers
In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into prote…
Protein Secondary Structure PredictionProtein Language ModelBoosting In-Silicon Directed Evolution with Fine-Tuned Protein Language Model and Tree Search
Protein evolution through amino acid mutations is a cornerstone of life sciences. Recent advances in protein language models have shown rich evolutionary patterns, offering unprecedented potential for in-silicon directed…
Protein Language ModelReinforcement LearningLow-N Protein Activity Optimization with FolDE
Proteins are traditionally optimized through the costly construction and measurement of many mutants. Active Learning-assisted Directed Evolution (ALDE) alleviates that cost by predicting the best improvements and iterat…
Protein Language ModelActivity PredictionActive LearningRelaxed Sequence Sampling for Diverse Protein Design
Protein design using structure prediction models such as AlphaFold2 has shown remarkable success, but existing approaches like relaxed sequence optimization (RSO) rely on single-path gradient descent and ignore sequence-…
Protein Language ModelProtein DesignSAE-RNA: A Sparse Autoencoder Model for Interpreting RNA Language Model Representations
Deep learning, particularly with the advancement of Large Language Models, has transformed biomolecular modeling, with protein language models such as ESM inspiring emerging RNA language models such as RiNALMo. Recent wo…
Protein Language ModelInstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions
Multimodal protein language models deliver strong performance on mutation-effect prediction, but training such models from scratch demands substantial computational resources. In this paper, we propose a fine-tuning fram…
Protein Language ModelFrom Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
Protein language models (PLMs) have advanced computational protein science through large-scale pretraining and scalable architectures. In parallel, reinforcement learning (RL) has broadened exploration and enabled precis…
Protein Language ModelReinforcement LearningProtein DesignLAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models
Identifying DNA- (DBPs) and RNA-binding proteins (RBPs) is crucial for the understanding of cell function, molecular interactions as well as regulatory functions. Owing to their high similarity, most of the existing appr…
Protein Language ModelMulti-Label LearningIs Sequence Information All You Need for Bayesian Optimization of Antibodies?
Bayesian optimization is a natural candidate for the engineering of antibody therapeutic properties, which is often iterative and expensive. However, finding the optimal choice of surrogate model for optimization over th…
Protein Language Model