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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 351–360 of 691 papers

TitleStatusHype
A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
3D Molecular Geometry Analysis with 2D Graphs—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
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