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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 411–420 of 691 papers

TitleStatusHype
Material Microstructure Design Using VAE-Regression with Multimodal Prior—0
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey—0
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
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
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