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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 76–100 of 691 papers

TitleStatusHype
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionCode1
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
FunQG: Molecular Representation Learning Via Quotient GraphsCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Dual-view Molecule Pre-trainingCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Directed Graph Grammars for Sequence-based LearningCode1
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
E(n) Equivariant Topological Neural NetworksCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Graph Transformers for Large GraphsCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
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