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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
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction ClassificationCode0
Structure-based drug design with geometric deep learning—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
Substructure-Atom Cross Attention for Molecular Representation Learning—0
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
Improving Molecular Pretraining with Complementary FeaturizationsCode0
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Graph Neural Networks for Molecules—0
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients—0
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