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feat: enforce config filters, add run manifest, bench leakage CLI, 37 tests - #1
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…pand tests - Enforce min_length/max_length from config in score_candidates() - Apply min_novelty and max_safety_risk selection thresholds from config - Add `valid` field to ScoredCandidate; mark invalid sequences with failure reasons - Add `selected` boolean to JSONL output rows - Generate run_manifest.json with run_id, input SHA-256 hashes, config hash, pipeline version - Add `openamp-foundry bench leakage` CLI subcommand and `make bench-leakage` target - Improve report disclaimer to explicitly state no antimicrobial activity demonstrated - Expand test suite from 6 to 37 tests covering: pipeline filters, selection thresholds, run manifest generation, benchmark leakage detection, splits, evaluation, CLI integration
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cschanhniem
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Jun 28, 2026
* feat: elastase resistance + aggregation propensity scoring Two new computational features to reduce wet-lab failure rate: 1. Elastase resistance (GAP #3 from audit): - physchem.py: ELASTASE_SITES = {A,V,S} (HNE primary P1 substrates); interior_protease_sites() reused; elastase_site_density and interior_elastase_sites added to compute_features() output. - stability.py: serum_stability_score() extended from 2-protease (trypsin/chymotrypsin) to 3-protease model. Weighted sum with trypsin:2 > chymotrypsin:1 > elastase:0.5. Helix-forming AMPs with high Ala content are now correctly penalised at infection sites where HNE is abundant (>1 µM). Denominator 3.5 = sum of weights; backward-compatible (missing elastase key → 0.0). - Literature: Bieth (1986); Doherty et al. (1991 Biochemistry). - 12 new tests in test_elastase_stability.py. 2. Aggregation propensity (GAP #1 from audit): - physchem.py: AGG_HYDROPHOBIC = {V,I,L,M,F,W}; new function aggregation_propensity() — two-component model: 0.7 × interior_run_risk (run ≥ 4 → ramp 0→1 over 5 residues) + 0.3 × beta_branched_density_risk (V,I,T > 20% → ramp 0→1) Returns [0,1]; key added to compute_features() output. - synthesis.py: synthesis_feasibility_score() now includes: if agg > 0: score -= min(agg * 0.25, 0.20) Max penalty = 0.20 (capped). Backward-compat (missing key → 0). - Literature: Quittot et al. (2017 Protein Sci); Wurth et al. (2006 J Mol Biol). - 22 new tests in test_aggregation_propensity.py. Impact: AUROC=0.814 (unchanged; elastase/aggregation only affect synthesis and stability, not the activity score that drives AUROC). Total test count: 1122. * fix: address code review HIGH issues for PR #49 - physchem.py: aggregation_propensity() now runs hydrophobic-run check on the FULL sequence (was interior-only), aligning with QC regex HYDROPHOBIC_RUN_RE which also scans the full sequence. Docstring claim "same threshold as QC HYDROPHOBIC_RUN_RE flag" is now factually true. Also: fixed saturation comment "run ≥ 9" → correct "run ≥ 8"; removed redundant `if max_run >= 4` guard (max(0, ...) already handles it); added Ala limitation note to docstring (Ala aggregation not modelled). - test_aggregation_propensity.py: replaced test_run_of_4_triggers_risk (which was passing for wrong reason — via beta_risk, not run_risk) with three tests: test_run_of_4_triggers_run_risk (asserts score > 0.14 which guarantees the run component), test_run_of_4_boundary_exact_run_component (verifies exact math: KVLLLK → run=4 → run_risk=0.14), and test_run_of_8_saturates_at_max_run_risk (verifies saturation at run=8). - Makefile: update test count to 1124.
This was referenced Jul 1, 2026
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Summary
min_length/max_lengthfromconfigs/pipeline.yamlnow filter sequences in the pipeline; invalid sequences getvalid=Falseand zero activity score with failure modes loggedmin_noveltyandmax_safety_riskfrom config now gate which candidates are selected for evidence certificates (e.g. near-duplicate reference copies are excluded from selection)run_manifest.jsonwithrun_id,pipeline_version,config_hash, per-input SHA-256 hashes, output paths, and timestamp — satisfying the reproducibility requirementbench leakageCLI command:openamp-foundry bench leakage --candidates ... --references ...detects near-duplicate contamination and warns when benchmark results may be inflated; also available asmake bench-leakageselectedfield in JSONL output: Each row now includes a boolean showing whether the candidate passed all filters and was selected for evidence certificatesTest plan
make test— all 37 tests passmake demo— demo pipeline runs end-to-end, produces ranked JSONL, report, evidence certificates, and run manifestmake bench-leakage— detects 3 near-duplicate candidates in demo dataset with warningschemas/candidate.schema.jsonmin_novelty: 0.20configSafety
No changes to safety policy, allowed amino acids, or optimization objectives. All new code adds filtering (removes unsafe/low-quality candidates) rather than relaxing constraints.