Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
It is demonstrated that encoding conformational ensembles into a single thermodynamically informed embedding improves cyclic-peptide property prediction.
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It is demonstrated that encoding conformational ensembles into a single thermodynamically informed embedding improves cyclic-peptide property prediction.
This work presents a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail, and connects evaluation choices to real-world applications and case studies encountered in pharmaceutical research.