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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 541–550 of 691 papers

TitleStatusHype
Learning Invariances in Neural Networks from Training Data—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
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