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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 241–250 of 691 papers

TitleStatusHype
Molecular Graph Contrastive Learning with Line GraphCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Molecular geometric deep learningCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property PredictionCode0
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
Equivariant Message Passing Neural Network for Crystal Material DiscoveryCode0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
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