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Property Prediction
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Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.
논문
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Heat Kernel Goes Topological
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Large Language Model Agent for Modular Task Execution in Drug Discovery
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
Descriptor-based Foundation Models for Molecular Property Prediction
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Graph Neural Networks in Modern AI-aided Drug Discovery
Positional Encoding meets Persistent Homology on Graphs
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers
Recent Developments in GNNs for Drug Discovery
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages
Directed Graph Grammars for Sequence-based Learning
Graph Positional Autoencoders as Self-supervised Learners
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
All You Need Is Synthetic Task Augmentation
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning
Quotient Complex Transformer (QCformer) for Perovskite Data Analysis
Pure Component Property Estimation Framework Using Explainable Machine Learning Methods
Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
Soft causal learning for generalized molecule property prediction: An environment perspective
Multi-modal cascade feature transfer for polymer property prediction
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