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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 201–210 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
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