Implement and execute a small, reproducible RDKit-descriptor + LightGBM baseline on the released ExpansionRx split: train (5,326 rows) and test (2,282 rows). These labels are public; this is retrospective evaluation, not a blind competition entry. The reference source_manifest pins the releases and attribution, data_card defines endpoint units, and tutorial contains the upstream notebook and evaluation code.
Use only train labels for fitting, feature preprocessing, model selection and early stopping. Use no external labels, pretrained representations or leaderboard tuning. Freeze one recipe and record its seed before scoring test. Train one model per endpoint on available training measurements. Follow the tutorial's descriptor/LightGBM approach; make any change needed for reproducibility explicit. Add a training-median predictor as a comparator. Preserve the original split and every test identifier; report duplicate IDs, canonical-isomeric-SMILES overlap and invalid molecules, including a documented fallback prediction when necessary. Do not silently drop difficult cases or relabel a public test as hidden.
Use the nine named endpoints in document_schema. For modeled targets use LogD unchanged and log10(x+1) for the other endpoints (the tutorial's unit offsets are permissible if exactly inverted). Return predictions in the source units. For scoring use LogD unchanged and log10(max(x,0)+1) otherwise, as in the bound evaluator. Report observed-label counts, point MAE and RAE = sum(abs(pred-true))/sum(abs(true-mean(true))) for each endpoint, and the unweighted mean of the nine RAEs. Report null with a reason for undefined denominators; never coerce them to zero. Distinguish point estimates from the upstream bootstrap-mean implementation. Compute fixed-seed 1,000-resample intervals as additional evidence, reporting exactly what interval is used. Include the median baseline under the same evaluation. A good score is not a condition of acceptance.
Submit document JSON matching document_schema; predictions CSV with exactly all 2,282 test IDs, SMILES and all nine source endpoint columns; and a reproducibility ZIP containing runnable source, a pinned environment, a one-command rerun, run logs, split/data audit, and a concise report. Commands must use the bound input files without silently fetching changing data. Re-running evaluation on the submitted predictions must reproduce reported point metrics to 1e-8. Document library/platform-dependent training variation separately.
The machine check validates report structure only. Human review checks the actual run, units, complete predictions, train/test separation, source fidelity and reproducibility. This is a complete baseline task, not merely a proposed method. raw is provided only for documenting censoring and provenance; do not train on it because it includes held-out molecules. Inspect source_manifest and retain its CC-BY-4.0 attribution. After starting an Attempt, record working findings and end it with a summary.