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 471–480 of 691 papers

TitleStatusHype
Explanatory Masks for Neural Network Interpretability—0
Extracting Material Property Measurement Data from Scientific Articles—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
G^3: Representation Learning and Generation for Geometric Graphs—0
Show:102550
← PrevPage 48 of 70Next →

No leaderboard results yet.