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Property Prediction

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.

Papers

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Showing 1–25 of 691 papers

TitleStatusHype
Heat Kernel Goes Topological—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—0
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation—0
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation LearningCode0
Descriptor-based Foundation Models for Molecular Property PredictionCode2
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?—0
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Positional Encoding meets Persistent Homology on GraphsCode0
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder LayersCode0
Recent Developments in GNNs for Drug Discovery—0
GenIC: An LLM-Based Framework for Instance Completion in Knowledge GraphsCode0
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Graph Positional Autoencoders as Self-supervised Learners—0
Directed Graph Grammars for Sequence-based LearningCode1
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