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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 231–240 of 691 papers

TitleStatusHype
Graph Generative Pre-trained Transformer—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
Graph Networks with Spectral Message Passing—0
Graph Neural Networks Go Forward-Forward—0
Graph Positional Autoencoders as Self-supervised Learners—0
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