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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 291–300 of 691 papers

TitleStatusHype
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Invariance-Aware Randomized Smoothing Certificates—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
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