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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 11–20 of 691 papers

TitleStatusHype
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
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