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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 261–270 of 691 papers

TitleStatusHype
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Heat Kernel Goes Topological—0
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Equivariant Neural Tangent Kernels—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Equivariant Networks for Crystal Structures—0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
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