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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 141–150 of 691 papers

TitleStatusHype
Publishing Neural Networks in Drug Discovery Might Compromise Training Data PrivacyCode1
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentationCode1
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
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityCode1
HELM: Hierarchical Encoding for mRNA Language Modeling—0
Large-Scale Knowledge Integration for Enhanced Molecular Property PredictionCode0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
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