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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 551–575 of 691 papers

TitleStatusHype
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
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
Contextualized Messages Boost Graph RepresentationsCode0
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
Temporal graph models fail to capture global temporal dynamicsCode0
Equivariant Message Passing Neural Network for Crystal Material DiscoveryCode0
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific AdaptationCode0
Enhancing Molecular Property Prediction via Mixture of Collaborative ExpertsCode0
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
testRNN: Coverage-guided Testing on Recurrent Neural NetworksCode0
Embedding Graphs on Grassmann ManifoldCode0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Conditional molecular design with deep generative modelsCode0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningCode0
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point CloudsCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling PerspectiveCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictionCode0
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
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