HTML Report

After each pipeline run, HEDGEHOG generates a self-contained interactive HTML report at:

results/run_N/report.html

The report uses Plotly.js  for interactive charts and includes a model filter dropdown that lets you view metrics for individual generative models or compare all models side-by-side.

Report Sections

Generator Reality Assessment

Generator Reality Assessment scorecard in RUN_INFO.md is an optional chart in the HTML report when scoring data is available.

It summarizes each model with:

• Generator Reality Score (0.0–100.0) used for ranking how well the model’s initial generated set survives the pipeline gates.
• Final Candidate Pool Quality (0.0–100.0), a secondary survivor-pool score for the molecules that already passed the pipeline.
• Grade (Excellent, Strong, Moderate, Weak) for quick triage.
• Confidence (High, Medium, Low) for reliability assessment.
• Main Bottleneck list to show the weakest components.

How the score is assembled

The score is collected per model_name. If a CSV does not have model_name, HEDGEHOG treats it as an aggregate __all__ model. This is an explainable ranking scorecard to compare models inside the same experiment and then validate best candidates with the underlying stage level reports. Component scores are normalized to 0..100.

The report generator reads these sources:

The final model score is:

overall = sum(component_weight * component_score) / sum(available_component_weights)

Missing components are not scored as zero. They are marked available: false, excluded from the denominator, and recorded as warnings where appropriate, and the remaining weights are renormalized automatically. Confidence drops when too few components or final molecules are available. If evidence is sparse (final molecules is low and/or several components are missing), Confidence drops from High to Medium or Low.

The generator score also supports hard caps for critical funnel failures. By default, a structural stage pass rate below 0.20 caps the score at 60, a descriptor all-pass rate below 0.50 caps it at 70, and a final retention rate below 0.05 caps it at 70. These caps prevent a model from looking strong when an AND-gate stage rejects most of its generated molecules.

Final Candidate Pool Quality inspect the final pool: yield uses final-count saturation, physchem uses the mean descriptor flag pass rate, structural uses the mean structural flag pass rate, and synthesis/docking/diversity use the same component formulas.

To configure or disable this section, use:

config_weighted_score: src/hedgehog/configs/config_weighted_score.yml

Set run: false in that file to remove the generator reality assessment from the report.

You can tune docking/synthesis behavior in this config to move rankings toward affinity-rich or synthetic tractability-focused models:

• Docking thresholds are configured under docking (bad_affinity/good_affinity, bad_cnnscore/good_cnnscore, bad_cnnaffinity/good_cnnaffinity).
• Synthesis thresholds are configured under synthesis (sa_*, ra_*, syba_*, target_search_time_sec).

Pipeline Flow (Sankey Diagram)

Report displays a Sankey diagram showing how molecules flow through the pipeline. Each node represents a pipeline stage, and the width of each link is proportional to the number of molecules that survive that transition.

• Purple links represent molecules that pass to the next stage.
• Gray “Lost” nodes branch off at each transition, showing how many molecules were filtered out.
• Hover over any link or node to see exact counts and percentages relative to the initial set.

A classic funnel chart is also included, showing the absolute molecule count at each stage with percentage-of-initial annotations.

Executive Summary

Four summary cards display key pipeline statistics:

A stage status table shows each stage as COMPLETED, FAILED, or DISABLED.

Model Comparison

When the input contains molecules from multiple generative models, the report includes:

• Grouped bar chart comparing initial vs. final molecule counts per model with retention rates.
• Stacked bar chart showing where molecules were lost (by stage) for each model.
• A model dropdown at the top of the report to filter all sections by a single model or compare all.

Descriptor Analysis

Two descriptor sections appear in the report — one for initial descriptors (computed early in the pipeline) and one for final descriptors (recomputed on surviving molecules). Bar plots and Summary table are broken down by model.

Drug-likeness threshold lines are included as reference markers in the interactive histograms.

Interpreting Comparison Histograms

When comparing generated molecules against reference sets:

• Overlapping distributions indicate that generated molecules match the reference property profile.
• Shifted distributions highlight systematic differences (e.g., generated molecules are heavier or more lipophilic than references).
• Use the model list to isolate individual models and see which generator best matches the target property space.

Structural Filters Analysis

This section covers the structural filtering stages.

• Heatmap of banned ratios (fraction of molecules failing each filter) by model.
• Common Alerts breakdown with detailed pass rates per rule set.

Synthesis Analysis

The synthesis section presents scores from calculated scores with Bar plots for each score distribution.

Docking Results

The docking section reports binding affinity results from GNINA, SMINA, and Matcha:

• Box plots of affinity scores grouped by model.
• Top molecules table listing the best-scoring compounds with their affinities.

Docking Filters

If docking filters are enabled, this section shows:

• Per-filter pass/fail stats for each enabled filter .
• Histograms of numeric metrics with threshold lines.
• Pass rate by model of total poses and pass rates.
• Interaction profile (ProLIF) when interaction reporting is enabled: top-contact residues, interaction type distribution, and residue × interaction-type heatmap.

Generative Metrics (MolEval)

This section reports intrinsic distribution quality metrics computed by the vendored MolEval library. See the MolEval Metrics page for detailed metric definitions.

Output Files

After report generation, the run directory contains:

Configuration

Report generation is triggered automatically by hedgehog after all pipeline stages complete. The MolEval sections require a valid config_moleval path in the pipeline configuration:

config_moleval: src/hedgehog/configs/config_moleval.yml

See MolEval Configuration for all available options.