Frozen tasks, score tables, synthon spaces and shared warm-start seeds for SynthonBench: a benchmark for budgeted search over synthon combinatorial (make-on-demand) chemical spaces.

Search methods over synthon spaces never enumerate the full product library; they propose reaction-component tuples (reaction_id, synthon_ids) and pay a fixed oracle-call budget. SynthonBench scores such methods fairly and reproducibly against frozen score tables and the top-k DCRF efficiency metric (random search ≈ 1.0).

Companion code: synthonbench (pip install synthonbench). The package downloads this dataset for you:

pip install "synthonbench[hf]"
synthonbench download --scale 1M            # spaces + glide_1M + surrogate_1M + seeds
synthonbench download --scale all           # everything, including the 100M table (~5 GB)

Targets & scales

Three Glide docking targets — KIF11, PYRD, TGFR1 — over the same 42 canonical reactions, at three scales of the synthon product space:

scale	products	unique synthons
1M	990,610	6,270
10M	10,022,100	13,880
100M	99,851,700	31,993

Files

spaces/
  synthon_space_{1M,10M,100M}.synthons.tsv    # smiles  synthon_id  position  reaction_id
  synthon_space_{1M,10M,100M}.properties.csv  # synthon_id + synthetic per-synthon attributes
  reactions.tsv                               # 42 canonical reactions (SMIRKS) + reagent patterns
scores/
  glide_1M.parquet          # REAL Glide @1M, headline docking oracle (LE/medchem-filtered), all 3 targets
  glide_1M.parquet.sidx/    # mmap product_id -> score index (bounded-memory oracle lookup)
  glide_1M.parquet.topk/    # top-100k product cache per score column (exact top-k for DCRF)
  surrogate_1M.parquet      # LightGBM docking surrogate, all 3 targets
  surrogate_1M.parquet.sidx/    surrogate_1M.parquet.topk/
  surrogate_10M.parquet     (+ .sidx/ .topk/)
  surrogate_100M.parquet    # ~4.6 GB  (+ .sidx/ ~3.8 GB, .topk/)
seeds/
  seeds_<target>_s{0,1,2}.parquet   # 3 shared 100k warm-start pools per target (one per protocol seed)

Sidecars (`.parquet.sidx`, `.parquet.topk`)

Large score tables are not loaded into a Python {product_id: score} dict. Each table ships two bounded-memory sidecars that synthonbench reads directly:

.sidx — a memory-mapped product_id → score index. synthonbench.load_oracle returns an IndexedScoreTableOracle backed by it, so oracle lookups never materialise the table.
.topk — the best 100k products per (score column, direction), so top-k recall / DCRF and the reported docking-score means need no full-table sort.

synthonbench download fetches these alongside each parquet automatically. If you stage a parquet by hand, rebuild them with synthonbench build-score-index and synthonbench build-topk-cache (the glide table needs _glide_le@maximize + _glide_raw@minimize; the surrogate tables need _predicted_dock_score@minimize, for each of the three targets).

`scores/glide_1M.parquet` (headline real-Glide oracle)

One row per product (990,610). Keyed by product_id (md5 of the assembled product) which also keys every surrogate table.

column	meaning
`product_id`, `reaction_id`	product id and its reaction
`smiles`, `canonical_smiles`	assembled product
`<target>_glide_raw`	raw Glide docking score
`<target>_glide_le`	benchmark score: medchem-filtered, lipophilicity-penalized (−Glide−cLogP) ligand-efficiency score; `0.0` if the product fails the medchem filter or docking
`<target>_le_ratio`, `<target>_valid`	ligand efficiency and validity flag
`passes_exp27_exact_filter`, `exp27_filter_reason`	medchem filter outcome
`mol_wt`, `mol_logp`, `hba`, `hbd`, `rotatable_bonds`, `fraction_csp3`, `tpsa`, `aromatic_rings`, `fluorine_count`, `amide_count`	RDKit physchem descriptors

<target> ∈ {kif11, pyrd, tgfr1}.

`scores/surrogate_{1M,10M,100M}.parquet`

column	meaning
`product_id`, `reaction_id`, `smiles`	product identity
`synthon1_id`, `synthon2_id`, `synthon3_id`	the synthon tuple (`synthon3` null for 2-component reactions)
`kif11_predicted_dock_score`, `pyrd_predicted_dock_score`, `tgfr1_predicted_dock_score`	LightGBM surrogate scores

`seeds/seeds_<target>_s{0,1,2}.parquet` (shared warm-start pools)

Three independent uniform-random 100k samples of the 1M product space per target — one per protocol seed 0,1,2. Every method may initialize from the same pool so warm-start does not confound the comparison. Columns: target, seed, product_id, reaction_id, synthon1_id, synthon2_id, synthon3_id, smiles, surrogate_score, glide_le_score. (The maximum benchmark budget is 100k oracle calls, so a full seed pool is itself within budget.)

Quick load

from datasets import load_dataset
glide = load_dataset("mireklzicar/synthonbench", "glide_1M", split="train")
seeds = load_dataset("mireklzicar/synthonbench", "seeds", split="train")

or directly with pandas / pyarrow:

import pandas as pd
df = pd.read_parquet("hf://datasets/mireklzicar/synthonbench/scores/glide_1M.parquet")

Provenance & licensing

Synthon spaces are derived from publicly described make-on-demand reaction schemes; real-Glide scores were produced with Schrödinger Glide and the LightGBM surrogate was trained on them. The scores and synthon tables released here are redistributable under CC-BY-4.0. Docking/space construction software (Glide, BiosolveIT FTrees/CoLibri, SpaceHASTEN) is not included and requires the respective vendor licenses; see the code repository for how external methods are wired in optionally.