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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

Showing 661670 of 691 papers

TitleStatusHype
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studyCode0
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation LearningCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
GenIC: An LLM-Based Framework for Instance Completion in Knowledge GraphsCode0
Subgraph Aggregation for Out-of-Distribution Generalization on GraphsCode0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
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