# Manuscript experiments and commands

The manuscript contains unfilled scientific-result tables and empirical plot layouts. Executed reference runs are stored separately under `results/`. Their graph sizes and metrics are listed in `validation.md`.

| Manuscript item | Workflow | Remaining experiment |
|---|---|---|
| Figure 1 | Editable TikZ and molecule-rendering source in `assets/figure1_source/` | Final formulation diagram included |
| Table 1, discrete generation | `run_benchmarks.py` with `toy.yaml`, `grid.yaml`, `strings.yaml` | Larger held-out graph-instance study, SubTB, and learned-backward TLM comparator |
| Figure 2, distribution diagnostics | `sweep_exact.py`, saved `training.json`, `plot_results.py` | Full five-seed sweep over all specified graph families |
| Table 2, molecular construction | `run_molecular.py` with `molecular_public.yaml` or `enamine_local.yaml`; `evaluate_candidates.py` | Licensed inventory and matched third-party generators |
| Table 3, molecular properties | `fit-properties`, `score`, generation, candidate evaluation | LIT-PCBA docking, full target panel, and external baselines |
| Figure 3, preferences and scaling | `sweep_preferences.py`; repeated local-inventory configurations | Full target-specific preference grid and measured catalog scaling |
| Table 4, ablations | `run_benchmarks.py --config configs/ablations.yaml` | Matched molecular ablations and larger graph instances |
| Table 5, scaling | `run_molecular.py` with parent limits 100, 1000, 10000 | Licensed catalog expansion, peak-memory measurements, full timing sweep |
| Supplement, reaction budgets | `molecular_public.yaml` runs B = 0, 1, 2 | Full target inventory and matched external methods |
| Supplement, target-specific docking | Common candidate records and receptor/grid protocol | Fifteen prepared target pockets and external generator integrations |
| Supplement, property prediction | `dooable fit-properties` | Five split seeds and selected released molecular encoder |

## Implemented comparisons

`uniform` assigns equal mass to outgoing actions. `reference_tilt` reweights that path process by terminal reward through a backward value recursion. `tb_uniform` learns a forward trajectory-balance sampler against locally uniform backward probabilities. `dooable` fits the cost-dependent backward prefix values and forward trajectory balance. `exact` uses both dynamic programs.

`zero_cost` sets every edge cost to zero for the conditional route law. `duplicate_endpoints` assigns a separate terminal reward to each incoming stop edge and evaluates the resulting policy after canonical merging. `unnormalized` uses learned prefix differences directly in the backward path weights during trajectory-balance training. `tb_exact` uses the exact cost-dependent backward policy during forward learning.

The cost-free and duplicated-endpoint comparisons use exact forward construction to isolate the corresponding distributional change. Their runtimes therefore describe the exact ablation, while learned-policy runtimes include training. Comparisons of optimization speed should group methods by solver mode.

## External comparisons

The following original implementations require integration with the selected inventory and scoring protocol. They have not been run in this package.

| Method | Verified primary publication | Required integration |
|---|---|---|
| SynFlowNet | ICLR 2025, `synflownet` citation | Parent selection, matched reaction/reagent inventory, candidate and route export |
| RxnFlow | ICLR 2025, `rxnflow` citation | Inventory conversion, reward callback, route export |
| CGFlow | ICML 2025, `cgflow` citation | Prepared target pockets, pose handling, matched synthesis actions |
| SyntheMol | Nature Machine Intelligence 2024, `synthemol` citation | Matched construction inventory, score callback, search-call accounting |
| SubTB | ICML 2023, `subtb` citation | Identical graph and reward, original subtrajectory objective |
| TB with learned backward TLM | ICLR 2025, `backward` citation | Original backward-policy training objective |

These method names refer to their original published implementations. External outputs should provide one canonical structure and its full construction record per candidate, with method, seed, target, budget, requested count, and oracle-call accounting. Adapt native route records to the `actions` schema in `data.md` before replay.

## Scoring and split protocol

The public example uses measured BACE inhibition and Caco-2 transport to fit separate property predictors. Full target-pocket experiments require target-specific docking. Fix receptor preparation, protonation, ligand preparation, docking grids, Vina version, exhaustiveness, and random seeds before comparing methods. Docking energies use kcal/mol. Record raw energies separately from normalized reward utilities.

Select clipping bounds and hyperparameters on the validation partition. Group parents by Bemis-Murcko scaffold before generating descendants. Resolve products shared across parent partitions into one partition before held-out evaluation. The included small graph experiments optimize one supplied graph per run. Generalization across disjoint graph instances is a separate experiment.

Count every unique oracle evaluation, including scores used during support construction or preprocessing. Store cached scores and reuse them within a declared experiment. For the 168-outcome public graph, the scoring step evaluates all 168 outcomes before training. Sampling counts are recorded separately.

The current public evaluator reports two-dimensional hypervolume over predicted BACE and Caco-2 utilities. The full docking study uses affinity and permeability utilities on the same normalized axes. QED and internal diversity are additional readouts. Report undefined summaries as missing when fewer than the required candidates are available.

## Citation verification

`bibliography/verified_references.csv` and `.json` contain 72 references, each with authors, publication year, venue, primary publication URL, metadata source, and verification date. Publisher BibTeX, official conference records, journal citation metadata, and publisher-deposited Crossref records were used. Conference versions replace corresponding preprints. Strong Stochastic Flow Maps is identified as an ICML workshop contribution.