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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 376–400 of 691 papers

TitleStatusHype
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction—0
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Equivariant Neural Tangent Kernels—0
Topological Neural Networks go Persistent, Equivariant, and Continuous—0
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property PredictionCode0
A General Approach for Determining Applicability Domain of Machine Learning Models—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMACode0
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
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