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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 341–350 of 691 papers

TitleStatusHype
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Evaluating multiple models using labeled and unlabeled data—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Explanatory Masks for Neural Network Interpretability—0
Extracting Material Property Measurement Data from Scientific Articles—0
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