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Structure-activity relationship (SAR) analysis guide for drug discovery including molecular descriptor analysis, scaffold analysis, and activity cliff detection.
Passed 16 of 19 test cases with the skill, and 11 of 19 without it.
and 3 more in the full report
6 fixed13 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When structuring a written SAR report, what exact markdown header title must be used for the dedicated section at the end that discusses missing comparative controls and proposed analogues?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies using the exact header title `### Suggestions for Further Study`. | Missed | Passed |
The instructions your model reads when the skill loads.
Short Description: Comprehensive guide for performing Structure-Activity Relationship (SAR) analysis using RDKit.
Authors: Ohagent Team
Version: 1.0
Last Updated: December 2025
License: CC BY 4.0
Commercial Use: ✅ Allowed
Structure-Activity Relationship (SAR) analysis is a core medicinal-chemistry workflow that relates systematic structural variations of a chemical series to changes in biological activity. The goal is to (1) identify a common scaffold (Maximum Common Substructure, MCS) shared by a series of analogues, (2) decompose each molecule into the scaffold plus its R-group substituents, (3) align all molecules so substituents at equivalent positions are visually comparable, and (4) connect substituent variation to potency to derive testable design hypotheses.
This guide formalizes a reproducible RDKit-based SAR workflow that produces an interactive HTML report (compound table with aligned core/R-groups and an activity heatmap) and a written SAR narrative that explicitly contrasts substituents at the same R-position. It is intended for use on activity tables containing SMILES, a compound identifier, and a numeric potency value (IC50, Ki, EC50, %inhibition, etc.).
MCS is the largest connected substructure shared by all (or a configurable threshold of) molecules in a set. RDKit's rdFMCS.FindMCS searches for this scaffold under tunable atom/bond comparison rules. For SAR, MCS provides the anchor template against which every analogue is decomposed and aligned. A threshold=0.8 allows MCS to be defined when only 80% of molecules contain the candidate substructure, which is more robust to outliers than threshold=1.0. ringMatchesRingOnly=True and completeRingsOnly=True prevent partial-ring fragments that look chemically meaningless.
R-group decomposition (rdRGroupDecomposition.RGroupDecompose) maps each molecule onto the MCS core and assigns the non-core fragments to enumerated R-positions (R1, R2, …). The output is a per-molecule dictionary {Core, R1, R2, …}. Constant R-positions (where every molecule carries the same fragment) are uninformative for SAR and should be pruned from the report so attention focuses on the variable positions that actually drive activity.
For SAR visualization to be interpretable, the core and each R-group must be drawn in the same orientation as the parent molecule. The recommended pattern uses three fall-back strategies in order: (1) a direct GetSubstructMatch, (2) a re-match after AdjustQueryProperties(makeDummiesQueries=True) so R-group dummy atoms are treated as queries, and (3) a final attempt with useChirality=False. Once a match is found, atom coordinates are copied from the parent conformer onto the fragment. Without this, R-group cells are drawn in arbitrary canonical orientations and visual SAR is essentially impossible to read.
test cases passed, vs 11 / 19 without
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