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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 261–270 of 691 papers

TitleStatusHype
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersCode2
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation NetworksCode1
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific AdaptationCode0
Accelerating Material Property Prediction using Generically Complete Isometry InvariantsCode0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language ModelsCode0
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