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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 431–440 of 691 papers

TitleStatusHype
Towards deep generation of guided wave representations for composite materialsCode0
Implicit Convolutional Kernels for Steerable CNNsCode1
An open unified deep graph learning framework for discovering drug leadsCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
Invariance-Aware Randomized Smoothing Certificates—0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Molecular Joint Representation Learning via Multi-modal Information—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
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