SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–610 of 691 papers

TitleStatusHype
3D Pre-training improves GNNs for Molecular Property Prediction—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
A molecular hypergraph convolutional network with functional group information—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
G^3: Representation Learning and Generation for Geometric Graphs—0
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
Optimal Decision Making in High-Throughput Virtual Screening Pipelines—0
Prediction of properties of metal alloy materials based on machine learning—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
Show:102550
← PrevPage 61 of 70Next →

No leaderboard results yet.