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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 221–230 of 691 papers

TitleStatusHype
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation LearningCode0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic ScreeningCode0
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformationCode0
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
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