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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 241–250 of 691 papers

TitleStatusHype
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models—0
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictionCode0
MatMMFuse: Multi-Modal Fusion model for Material Property PredictionCode0
Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery—0
Towards Faster and More Compact Foundation Models for Molecular Property PredictionCode0
Learning Hierarchical Interaction for Accurate Molecular Property PredictionCode0
Supervised Pretraining for Material Property Prediction—0
Synergistic Benefits of Joint Molecule Generation and Property Prediction—0
Generative Deep Learning Framework for Inverse Design of Fuels—0
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)—0
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