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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 501–510 of 691 papers

TitleStatusHype
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
A graph representation of molecular ensembles for polymer property predictionCode1
Partial Product Aware Machine Learning on DNA-Encoded Libraries—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Graph Anisotropic DiffusionCode0
Learning to Split for Automatic Bias DetectionCode1
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