--- dataset_info: - config_name: bioformbench_real features: - name: protein_id dtype: string description: Protein identifier (traceable to source_id) - name: protein_mw_kda dtype: float32 description: Molecular weight (kDa) - name: protein_pi dtype: float32 description: Isoelectric point - name: protein_tm_baseline_c dtype: float32 description: Baseline melting temperature (C), no stabilizer - name: buffer_species dtype: string description: Buffer chemical - name: buffer_conc_mm dtype: float32 description: Buffer concentration (mM) - name: ph dtype: float32 description: Formulation pH - name: ionic_strength_mm dtype: float32 description: Ionic strength (mM) - name: osmolarity_mosm_kg dtype: float32 description: Osmolarity (mOsm/kg) - name: stabilizers_json dtype: string description: JSON dict of stabilizer name to concentration - name: temperature_c dtype: float32 description: Storage/measurement temperature (C) - name: stability_score dtype: float32 description: Normalized stability outcome, 0-1, higher = more stable - name: measured_aggregation_percent dtype: float32 description: SEC percent aggregation, where reported - name: measured_tm_shift_c dtype: float32 description: DSF Tm shift versus baseline, where reported - name: source_title dtype: string description: Title of the primary literature source (required; unsourced rows are excluded, see Data Integrity) - name: source_id dtype: string description: PubMed Central identifier of the primary source splits: - name: full num_examples: 49 - config_name: bioformbench_marketed features: - name: inn dtype: string description: Antibody International Nonproprietary Name - name: title dtype: string description: FDA label title (product name, manufacturer) - name: ph dtype: float32 description: Formulation pH parsed from the label - name: buffer_species dtype: string description: Buffer chemical, where fully resolved - name: buffer_conc_mm dtype: float32 description: Buffer concentration (mM), where fully resolved - name: surfactant dtype: string description: Surfactant identity, where present - name: fv_pi dtype: float32 description: Isoelectric point computed from the VH+VL Fv sequence (Biopython ProtParam) - name: fv_mw_kda dtype: float32 description: Fv molecular weight computed from sequence - name: source_text dtype: string description: Verbatim label sentence(s) the row was parsed from splits: - name: full num_examples: 165 license: cc-by-4.0 --- # BioFormBench: Two Open Benchmarks for Biologics Formulation Research This release contains **two** datasets, kept separate because they answer different questions and neither should be read as a substitute for the other. ## Data integrity note (please read before using either dataset) An earlier internal draft of BioFormBench-Real contained 67 rows. Auditing `source_title`/`source_id` provenance found that **18 of those rows had no citation** and used placeholder identifiers (`protein_1`–`protein_5`) with round, unverified descriptors (50.0 kDa / pI 7.2 / Tm 65.0 C) repeated across otherwise-different molecules. Those rows have been **removed**. The 49 rows in this release all trace to a PubMed Central ID and were checked against their source text. If you have a copy of the 67-row file, discard it and use this one. ## 1. BioFormBench-Real (49 rows, 13 proteins) Literature-mined formulation-outcome measurements (DSF Tm-shift, SEC % aggregation, turbidity) for 13 real proteins across 11 primary papers, each row traceable to its source. This is the dataset to use for any claim about *stability-optimal* formulation, but at n=13 proteins (9 with enough per-protein data for leave-one-protein-out evaluation) it is underpowered for a definitive protein-conditionality verdict — see the companion paper's Discussion for what scaling this further would require. ## 2. BioFormBench-Marketed (165 rows, 80 antibodies) — new in this release 165 formulation presentations for 80 FDA-approved antibody therapeutics, deterministically parsed from FDA Structured Product Labeling (DailyMed) and linked to real VH/VL variable-domain sequences from Thera-SAbDab. Every parsed field keeps its verbatim source sentence (`source_text`) for audit. To our knowledge this is the first open dataset connecting therapeutic antibody sequence to marketed formulation composition at this scale. This dataset encodes **what was chosen** for an approved product, not what is necessarily stability-optimal — marketed choices are also shaped by manufacturability, prior platform investment and regulatory precedent. Used at full sample size (n=80) with proper multiple-comparison correction and leave-one-protein-out validation, protein sequence identity does **not** detectably predict the chosen pH or buffer beyond a constant "platform" baseline (all sign-flip p > 0.8) — see the companion paper for the full statistical analysis, including a worked example of how a naive n=27 subsample of this same data produces a spurious, non-reproducible positive result (Spearman rho=-0.464, p=0.015, uncorrected) that a full-sample, corrected, out-of-sample analysis overturns. ## Use cases 1. Test protein-conditional formulation hypotheses at adequate statistical power, with the selection-artifact and platform-baseline results in the companion paper as a required comparison point, not merely a possible one. 2. Train/evaluate predictive or generative formulation models against a documented platform baseline rather than only random/uniform baselines. 3. Audit other mechanistic formulation simulators using the diagnostic described in the companion paper (dead-slot spread, interior-argmax fraction) against these real formulations. 4. Sequence-to-formulation representation learning (BioFormBench-Marketed includes Fv-derived descriptors so no additional sequence processing is required to get started). ## Files - `bioformbench_real.csv` — 49 rows, BioFormBench-Real (see schema above). - `bioformbench_marketed.csv` — 165 rows, BioFormBench-Marketed (see schema above; full computed-descriptor and label-parse columns included). ## Known limitations - BioFormBench-Real: n=13 proteins (9 LOPO-eligible) is small; treat any per-protein result as illustrative, and prefer the aggregate statistics with their reported confidence intervals. - BioFormBench-Marketed: pH is extracted with a confidence flag (`ph_is_range`/composition-sentence provenance in the full release columns); the low-confidence stratum is retained rather than silently dropped, and the companion paper shows the main finding (no sequence effect) holds in the high-confidence stratum alone. Only 27–28 of 80 molecules have a fully resolved buffer species *and* concentration; buffer-level analyses should use that subset and are correspondingly lower-powered. - Neither dataset should be used to generate or select actual formulations for laboratory or clinical use without independent verification; see the companion paper's Ethics statement. ## Citation ```bibtex @dataset{kumar2026bioformbench, title={BioFormBench: Two Open Benchmarks for Biologics Formulation Research}, author={Kumar, Bonthada Sravan}, year={2026}, publisher={Hugging Face Datasets}, url={https://huggingface.co/datasets/Sravankumarbonthada/BioFormBench} } ``` ## Sources BioFormBench-Marketed is built from public FDA Structured Product Labeling (DailyMed) and Thera-SAbDab (Raybould et al., Nucleic Acids Research 2020). BioFormBench-Real is mined from 11 open-access PubMed Central articles, each cited per-row via `source_id`. ## Contact Bonthada Sravan Kumar — sravansaijohn@gmail.com — issues via GitHub (Maheshbonthada/bioform-lm) ## License CC-BY-4.0 --- **Last updated:** September 8, 2026