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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

Showing 6170 of 691 papers

TitleStatusHype
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
Global Concept Explanations for Graphs by Contrastive LearningCode1
Kermut: Composite kernel regression for protein variant effectsCode1
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised LearningCode1
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024Code1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
ChemLLM: A Chemical Large Language ModelCode1
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation NetworksCode1
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