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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 201–225 of 691 papers

TitleStatusHype
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
A Review of Large Language Models and Autonomous Agents in ChemistryCode3
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network PotentialsCode3
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
Learning Molecular Representation in a CellCode1
Towards Neural Scaling Laws for Foundation Models on Temporal GraphsCode1
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
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property PredictionCode1
Topological Neural Networks go Persistent, Equivariant, and Continuous—0
TAGMol: Target-Aware Gradient-guided Molecule GenerationCode1
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Sheaf HyperNetworks for Personalized Federated Learning—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
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
E(n) Equivariant Topological Neural NetworksCode1
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