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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 151–175 of 691 papers

TitleStatusHype
A community-powered search of machine learning strategy space to find NMR property prediction modelsCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
Learning Molecular Representation in a CellCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
Directed Graph Grammars for Sequence-based LearningCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Generative Pre-Training from MoleculesCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Molecular Hypergraph Neural NetworksCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property PredictionCode1
Global Concept Explanations for Graphs by Contrastive LearningCode1
Dual-view Molecule Pre-trainingCode1
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