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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 371–380 of 691 papers

TitleStatusHype
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?Code0
Token-Mol 1.0: Tokenized drug design with large language model—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
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
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