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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 321–330 of 691 papers

TitleStatusHype
MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property PredictionCode0
Subgraph Aggregation for Out-of-Distribution Generalization on GraphsCode0
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Homomorphism Counts as Structural Encodings for Graph LearningCode0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
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