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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 341–350 of 691 papers

TitleStatusHype
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational GraphsCode1
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
On Data Imbalance in Molecular Property Prediction with Pre-training—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Evaluating the diversity and utility of materials proposed by generative models—0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point CloudsCode0
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