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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 251–260 of 691 papers

TitleStatusHype
MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
TxGemma: Efficient and Agentic LLMs for Therapeutics—0
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning—0
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification—0
Multimodal machine learning with large language embedding model for polymer property predictionCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
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