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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 221–230 of 691 papers

TitleStatusHype
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
Graph Convolution: A High-Order and Adaptive Approach—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
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