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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 161–170 of 691 papers

TitleStatusHype
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries—0
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction—0
Self-supervised learning for crystal property prediction via denoising—0
Do Graph Neural Networks Work for High Entropy Alloys?Code0
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