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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 71–80 of 691 papers

TitleStatusHype
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
Integrating convolutional layers and biformer network with forward-forward and backpropagation trainingCode0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
MoMa: A Modular Deep Learning Framework for Material Property Prediction—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
MatterChat: A Multi-Modal LLM for Material Science—0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
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