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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 381–390 of 691 papers

TitleStatusHype
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Artificial Intelligence Enabled Material Behavior Prediction—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
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