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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 451–475 of 691 papers

TitleStatusHype
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
On Data Imbalance in Molecular Property Prediction with Pre-training—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions—0
Optimal Decision Making in High-Throughput Virtual Screening Pipelines—0
Orbital Graph Convolutional Neural Network for Material Property Prediction—0
Out-of-distribution materials property prediction using adversarial learning based fine-tuning—0
Partial Product Aware Machine Learning on DNA-Encoded Libraries—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction—0
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Polymer Informatics: Current Status and Critical Next Steps—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
PotentialNet for Molecular Property Prediction—0
Uncertainty quantification of molecular property prediction using Bayesian neural network models—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution—0
Prediction of properties of metal alloy materials based on machine learning—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
Pre-training of Molecular GNNs via Conditional Boltzmann Generator—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
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