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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
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Positional Encoding meets Persistent Homology on GraphsCode0
Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder LayersCode0
Recent Developments in GNNs for Drug Discovery—0
Graph Positional Autoencoders as Self-supervised Learners—0
GenIC: An LLM-Based Framework for Instance Completion in Knowledge GraphsCode0
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts—0
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