القائمة
Molecular graph representations and Bemis-Murcko scaffold splitting to assess OOD generalization.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Mitigating the effect of activity cliffs using Triplet Soft Margin (TSM) loss on High-Value Activity Cliff Triplets (HV-ACTs).
Running molecular geometry optimization and molecular dynamics (MD) simulations using ASE and MLIPs.
Generating 3D molecular conformations and preparing input files for quantum chemistry DFT calculations.
Constructing Gaussian output neural networks and training deep ensembles to quantify aleatoric and epistemic uncertainty.
Implementing Delta-ML (residual learning between low and high levels of theory) and model transfer learning.
Optimizing molecular feature weights and performing feature selection via Differentiable Information Imbalance (DII).
| name | molecular_graph_scaffold_split |
| description | Molecular graph representations and Bemis-Murcko scaffold splitting to assess OOD generalization. |
Use this skill when processing raw molecular data (SMILES or 3D coordinates) and preparing dataset partitions for machine learning training. This ensures realistic validation by avoiding chemical data leakage between splits.
Molecules are represented as graphs $G = (V, E)$, where:
Instead of random splitting, which can lead to over-optimistic performance estimates due to highly similar molecules appearing in both train and test splits, scaffold splitting partitions molecules by their core Bemis-Murcko scaffold (the ring structures and their linkers). This ensures:
Run the helper script scaffold_split.py to automatically calculate scaffold splits for a list of SMILES:
python scripts/scaffold_split.py --input dataset.csv --smiles_col "smiles" --ratios 0.8,0.1,0.1