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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 171–180 of 691 papers

TitleStatusHype
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Lo-Hi: Practical ML Drug Discovery BenchmarkCode1
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
Dual-view Molecule Pre-trainingCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
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