SynthonBench — data

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
  surrogate_1M.parquet      # LightGBM docking surrogate, all 3 targets
  surrogate_10M.parquet
  surrogate_100M.parquet    # ~4.6 GB
seeds/
  seeds_<target>_s{0,1,2}.parquet   # 3 shared 100k warm-start pools per target (one per protocol seed)

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.

Citation

See CITATION.cff.