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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 676–691 of 691 papers

TitleStatusHype
Triangular Contrastive Learning on Molecular Graphs—0
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence—0
Two-Stage Pretraining for Molecular Property Prediction in the Wild—0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
TxGemma: Efficient and Agentic LLMs for Therapeutics—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
Uncertainty quantification of molecular property prediction with Bayesian neural networks—0
Synergistic Benefits of Joint Molecule Generation and Property Prediction—0
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules—0
Uni-Mol: A Universal 3D Molecular Representation Learning Framework—0
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction—0
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction—0
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