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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 326–350 of 691 papers

TitleStatusHype
Towards out-of-distribution generalizable predictions of chemical kinetics propertiesCode0
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
Transformers for molecular property prediction: Domain adaptation efficiently improves performanceCode0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine LearningCode0
Uncertainty quantification for predictions of atomistic neural networksCode0
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
Accelerating Material Property Prediction using Generically Complete Isometry InvariantsCode0
Integrating convolutional layers and biformer network with forward-forward and backpropagation trainingCode0
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE ModelCode0
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder LayersCode0
Unsupervised Musical Object Discovery from AudioCode0
Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property PredictionCode0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
Interpretation of Semantic Tweet RepresentationsCode0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property PredictionCode0
Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction TasksCode0
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged ZeolitesCode0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
Molecular Joint Representation Learning via Multi-modal Information—0
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
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