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Found 13 papers, 7 papers with code
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
Deep Manifold Transformation for Protein Representation Learning
Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks.
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Protein design involves generating protein sequences based on their corresponding protein backbones.
Segment Anything in Defect Detection
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities.
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
Lightweight Contrastive Protein Structure-Sequence Transformation
In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
Generative De Novo Protein Design with Global Context
Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Furthermore, we devise adversarial training scheme, dubbed \textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning and theoretically explain the reasons.
