Team SyL

In this project, we established an automated pipeline that seamlessly generates explicit-solvent molecular dynamics (MD) ensembles directly from SMILES inputs, extracts the full distributions of 3D descriptors, and enables interactive exploration of the resulting data. Applying this framework to 37 benchmark molecules spanning the entire flexibility spectrum revealed that increasing molecular flexibility strongly correlates with a larger individual frame variance in 3D descriptors, particularly 3D-PSA (Spearman ρ ≈ 0.5). Furthermore, we demonstrated that for highly flexible molecules, descriptor values derived from a single static structure frequently fail to represent the broader ensemble distribution. These empirical findings substantiate a fundamental, first-principles limitation of static single-conformation descriptors in predicting the physicochemical properties of large, flexible molecules, underscores the absolute necessity of ensemble-based computational approaches.

Team

Placement

Participant