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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

Showing 261270 of 691 papers

TitleStatusHype
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
Object-Centric Learning with Slot Mixture ModuleCode0
Embedding Graphs on Grassmann ManifoldCode0
MatMMFuse: Multi-Modal Fusion model for Material Property PredictionCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property PredictionCode0
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorsCode0
DScribe: Library of Descriptors for Machine Learning in Materials ScienceCode0
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and ReconstructionCode0
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