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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 251–260 of 691 papers

TitleStatusHype
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Chemical Property Prediction Under Experimental Biases—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating multiple models using labeled and unlabeled data—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
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