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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 651–691 of 691 papers

TitleStatusHype
Continuous Representation of Molecules Using Graph Variational Autoencoder—0
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks—0
Neural Message Passing on High Order Paths—0
Molecule Property Prediction and Classification with Graph HypernetworksCode0
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
Explanatory Masks for Neural Network Interpretability—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Towards Interpretable Molecular Graph Representation Learning—0
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
Molecule Property Prediction Based on Spatial Graph EmbeddingCode0
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling PerspectiveCode0
testRNN: Coverage-guided Testing on Recurrent Neural NetworksCode0
Artificial Intelligence Enabled Material Behavior Prediction—0
Path-Augmented Graph Transformer NetworkCode0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorsCode0
DScribe: Library of Descriptors for Machine Learning in Materials ScienceCode0
Uncertainty quantification of molecular property prediction with Bayesian neural networks—0
Deep Robust Subjective Visual Property Prediction in Crowdsourcing—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
Uncertainty quantification of molecular property prediction using Bayesian neural network models—0
Graph Convolutional Neural Networks for Polymers Property Prediction—0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine LearningCode0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media—0
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of CompoundsCode0
BayesGrad: Explaining Predictions of Graph Convolutional NetworksCode0
Conditional molecular design with deep generative modelsCode0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
PotentialNet for Molecular Property Prediction—0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
Graph Convolution: A High-Order and Adaptive Approach—0
Interpretation of Semantic Tweet RepresentationsCode0
Deep Learning for Computational Chemistry—0
Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction TasksCode0
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure PredictionCode0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
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