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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 551–600 of 691 papers

TitleStatusHype
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
Attentive Walk-Aggregating Graph Neural NetworksCode0
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
A molecular hypergraph convolutional network with functional group information—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
G^3: Representation Learning and Generation for Geometric Graphs—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Optimal Decision Making in High-Throughput Virtual Screening Pipelines—0
Chemical-Reaction-Aware Molecule Representation LearningCode1
Prediction of properties of metal alloy materials based on machine learning—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
Generative Pre-Training from MoleculesCode1
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Hierarchical graph neural nets can capture long-range interactionsCode1
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks—0
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
Generalization and Robustness Implications in Object-Centric LearningCode1
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation LearningCode0
Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Dual-view Molecule Pre-trainingCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Simple GNN Regularisation for 3D Molecular Property Prediction & BeyondCode1
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformationCode0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Artificial Intelligence in Drug Discovery: Applications and TechniquesCode1
MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing—0
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural NetworksCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
Multipath Graph Convolutional Neural NetworksCode0
Ranking Structured Objects with Graph Neural NetworksCode0
MEG: Generating Molecular Counterfactual Explanations for Deep Graph NetworksCode1
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction—0
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Knowledge-aware Contrastive Molecular Graph Learning—0
Molecular Representation Learning by Leveraging Chemical Information—0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
Assigning Confidence to Molecular Property PredictionCode1
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