Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?

Laura van Weesep; Riccardo Tedoldi; Jens Sjölund; Hossein Azizpour; Susanne Winiwarter; Ola Engkvist; Jon Paul Janet; Samuel Genheden

cs.CLOct 1, 2026

Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?

Authors: Laura van Weesep, Riccardo Tedoldi, Jens Sjölund, Hossein Azizpour, Susanne Winiwarter, Ola Engkvist, Jon Paul Janet, Samuel Genheden, +1 more

Organizations: Molecular AI, Discovery Sciences R&D, AstraZeneca · Department of Information Technology, Uppsala University · Robotics, Perception & Learning, KTH Royal Institute of Technology · Science for Life Laboratory, Stockholm, Sweden · Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca · Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden

Abstract

The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repositories and industrial screening databases suffer from missing, inconsistent, or conflated assay annotations. In this work, we quantify the extent of missing annotations in PubChem for the BioAssay Ontology (BAO) assay format and physical detection method fields and investigate whether open-source and proprietary large language models (LLMs) can reliably predict and audit metadata annotations directly from the assay text. In our assessment, we found that the annotation coverage across PubChem's $\sim$ 2 million bioassays is critically sparse, 36% lacking an assay format, 89% a BioAssay type, and >99.9% any BAO-mapped assay format or detection technology term. This motivates the need for automated test-metadata curation. Using evaluation sets derived from PubChem and ChEMBL, we assess the agreement of seven open-source and proprietary LLMs with existing silver labels. Recall is at least 0.96 for biochemical and cell-based assay formats, with a similar pattern for detection technology, although disagreements increase on under-represented classes. Manual inspection shows that many of these disagreements trace back to inconsistencies between silver sources rather than to LLM error. Moreover, in a qualitative study with a senior industrial curator, LLM-generated evidence prompted the expert to revise some of their own labels, showing LLMs can flag potentially mislabeled assays. Across the study, performance differences between proprietary and open-source models were small. Together, these results suggest LLMs can support the large-scale annotation and auditing of assay metadata, though per-class reliability estimates and targeted human review remain necessary before such labels enter downstream ML pipelines.

Figures & tables

Disagreement pattern	Count	%	Sent for review
LLMs support the BARD label against ChEMBL	17	40%	4
ChEMBL and BARD agree and LLMs disagree with both	10	23%	10
ChEMBL and BARD disagree and LLMs disagree with both (novel prediction)	2	5%	2
No BARD label available; ChEMBL-only ground truth	14	33%	1
Total	43	100%	17

Table 2: Categorization of the 43 ChEMBL assays where the majority of LLMs disagreed with the ChEMBL BAO assay format label, and how many of each pattern were sent for senior industrial curator review (Table 13 ).

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Field	Non-empty	% of Total	Unique Values	% Unique (of non-empty)
AID	1 994 310	100.0%	1 994 310	100.00%
Comment	1 769 245	88.7%	647 580	36.60%
Description	1 770 567	88.8%	173 040	9.77%
Name	1 770 568	88.8%	1 559 216	88.06%
Deposit Date	1 910 340	95.8%	1 979	0.10%
Assay Format	1 278 238	64.1%	3	0.00%

Appendix

Table 5 : Assay coverage and uniqueness for description, comment, and name fields (Total AIDs = 1 994 310)

Assay Format	With Comment	Total	Coverage	Percentage
Organism-based	609 321	663 381	91.85%	52.96%
Cell-based	541 113	614 738	88.02%	47.03%
Biochemical	101	119	84.87%	0.01%
Total	1 150 535	1 278 238	90.01%	100.00%

Appendix

Table 6 : Distribution of assay formats available after bulk-downloading and processing PubChem BioAssay, broken down by commented vs. total counts and comment coverage. With Comment counts assays that have a description, comment, or name; Total counts all assays in the category; Coverage is the annotation rate per category (With Comment / Total); Percentage is each category’s share of the commented subset.

BAO Assay Format	With Comment	Total	Coverage	Percentage
cell-based format	112	119	94.12%	44.80%
biochemical format: protein format: single protein format	91	103	88.35%	36.40%
biochemical format: protein format: protein complex format	27	34	79.41%	10.80%
cell based format	9	12	75.00%	3.60%
biochemical format: protein format: Single protein format	5	6	83.33%	2.00%
organism-based format	4	4	100.00%	1.60%

Appendix

Table 7 : Distribution of BAO Assay Formats available after bulk-downloading and processing PubChem BioAssay, broken down by commented vs. total counts and comment coverage. With Comment counts assays that have a description, comment, or name; Total counts all assays in the category; Coverage is the annotation rate per category (With Comment / Total); Percentage is each category’s share of the commented subset.

BAO Detection Technology	With Comment	Total	Coverage	Percentage
AlphaLISA: fluorescence intensity	1	1	100.00%	0.31%
absorbance	2	2	100.00%	0.63%
fluorescence: flow cytometry	37	37	100.00%	11.64%
fluorescence: fluorescence intensity	112	115	97.39%	35.22%
fluorescence: fluorescence polarization	21	24	87.50%	6.60%
fluorescence: fret: htrf	2	3	66.67%	0.63%

Appendix

Table 8 : Distribution of BAO Detection Technologies available after bulk-downloading and processing PubChem BioAssay, broken down by commented vs. total counts and comment coverage. With Comment counts assays that have a description, comment, or name; Total counts all assays in the category; Coverage is the annotation rate per category (With Comment / Total); Percentage is each category’s share of the commented subset.

BioAssay Type	With Comment	Total	Coverage	Percentage
Biochemical	1 866	3 155	59.14%	0.90%
Biochemical $\mid$ Cell-based	18	22	81.82%	0.01%
Biochemical $\mid$ Cell-based $\mid$ In vivo	1	1	100.00%	0.00%
Biochemical $\mid$ Cell-based $\mid$ Toxicity	2	3	66.67%	0.00%
Biochemical $\mid$ In vitro	24	48	50.00%	0.01%
Biochemical $\mid$ In vivo	68	68	100.00%	0.03%

Appendix

Table 9 : Distribution of BioAssay Types available after bulk-downloading and processing PubChem BioAssay, broken down by commented vs. total counts and comment coverage. With Comment counts assays that have a description, comment, or name; Total counts all assays in the category; Coverage is the annotation rate per category (With Comment / Total); Percentage is each category’s share of the commented subset.

Figure 1 : Figure was made by downloading the BAO complete OWL and creating the subtree in PowerPoint.

Figure 2 : Figure was made by downloading the BAO complete OWL and creating the subtree in PowerPoint.

Parameter	GPT-4o	Gemini 3.6 Flash	Claude Sonnet 4.6
Provider / route	Azure OpenAI	Vertex AI (via AI Gateway)	AWS Bedrock (via AI Gateway)
Endpoint / API	chat.completions	OpenAI-compatible passthrough	bedrock-runtime.converse
Model / deployment ID	gpt-4o	google/ gemini-3.6-flash	us.anthropic.claude-sonnet-4-6
Temperature	0.0	0.0	0.0
Max output tokens	provider default	provider default	provider default
JSON-mode enforced?	yes ( json_object )	yes ( json_object )	no (prompt-only)

Appendix

Table 10: Runtime configuration for the three proprietary API-served models. Temperature was fixed at 0.0 across all models to obtain deterministic (greedy) decoding for reproducibility. Maximum output length was left at the provider default: the response is a compact JSON object with three short fields, well below any provider cap.

Parameter	Gemma 4 31B	GPT-OSS 20B	Gemma 3 27B	Llama 3.3 70B
Ollama model tag	gemma4:31b	gpt-oss:20b	gemma3:27b	llama3.3:70b
Context window ( num_ctx )	9000	9000	9000	9000
Max output ( num_predict )	8192	8192	8192	8192
Temperature	0.0	0.0	0.0	0.0
Seed	42	42	42	42
Structured output	JSON-Schema	JSON-Schema	JSON-Schema	JSON-Schema

Appendix

Table 11: Runtime configuration for the four open-weights models served via Ollama v0.17.4 . All open-weights runs used JSON-Schema-constrained decoding (Ollama’s format= parameter), which enforces the enum of valid BAO labels at generation time.

Figure 5 : Pairwise Cohen’s $\kappa$ between the three proprietary LLMs (Claude Sonnet 4.6, Gemini 3.6 Flash, GPT-4o) and four open source LLMs (GPT-OSS 20B, Gemma 3 27B, Gemma 4 41B, Llama 70B) on the BAO detection technology task. Agreement patterns mirror the assay format results: near-perfect agreement.

AID	ChEMBL	BARD (if diff.)	Top pred.	Comment	$n_{\text{dis.}}$
1984	biochemical	cell-based	cell-based	LLMs support BARD label	7
2121	biochemical	cell-based	cell-based	LLMs support BARD label	7
2217	cell-based	biochemical	biochemical	LLMs support BARD label	7
2613	tissue-based	whole-cell lysate (subclass of cell-free)	cell-free	LLMs support BARD label	7
588382	organism-based	cell-based	cell-based	LLMs support BARD label	7
588766	cell-free	single-protein (part of biochemical)	biochemical	LLMs support BARD label	7

Appendix

Table 12: Manual inspection of the 43 assays where the majority of LLMs disagreed with the ChEMBL BAO assay format label. All labels are BAO assay formats; the “ format” suffix is omitted for space.

n_{\text{dis.}}

counts models disagreeing with ChEMBL,

n_{\text{al.}}

counts models agreeing with ChEMBL; seven models were attempted per row unless noted otherwise. Rows shaded in gray were sent for, and received, senior industrial curator review (Table 13 ).

AID	ChEMBL	Top pred.	Short title	Expert format	Expert reason	Comment
1470	biochemical	cell-based	Discovery of novel allosteric modulators of the M1 muscarinic receptor: Agonist NMS binding at M1	cell-free, but could also be biochemical	In protocol: “Membranes were prepared from M1-expressing CHO cells”; format appears to be cell membranes but could be classified as biochemical as well	Expert finds multiple labels plausible
492958	cell-based	organism-based	Counterscreen for AddAB inhibitors: absorbance-based bacterial cell-based high throughput dose response assay for inhibitors of bacterial viability	organism-based	see short title; you might even classify it as “organism based” since these are bacteria cells: later adapted when we discussed definition	Expert agrees with LLM majority: ChEMBL/BARD label incorrect (expert initially leaned towards ChEMBL, but adapted after discussion)
588769	cell-free	biochemical	Late stage assay provider results from the probe development effort to identify inhibitors of plasma platelet activating factor acetylhydrolase (pPAFAH): fluorescence-based dose response biochemical gel-based competitive Activity-Based Protein Profiling (ABPP) assay for HTS compounds	biochemical	fluorescence-based dose response biochemical gel-based competitive Activity-Based Protein Profiling (ABPP) assay for HTS compounds	Expert agrees with LLM majority: ChEMBL/BARD label incorrect
1913	biochemical	cell-free	Luminescence-based dose response biochemical high throughput screening assay for inhibitors of the Heat Shock Protein 90 (HSP90)	biochemical	see short title (it is using a “reticulocyte lysate” so classifying it as “cell-free” makes some sense as well)	Expert agrees with ChEMBL: LLM majority plausible
2693	cell-based	organism-based	Fluorescence Cell-Based Dose Screen to Determine Inhibitors of S. cerevisiae Viability	organism-based	see short title (but could be defined as organism based since it is an organism, S. cerevisiae), but later adapted when we discussed definition	Expert agrees with LLM majority: ChEMBL/BARD label incorrect (expert initially leaned towards ChEMBL, but adapted after discussion)
488745	organism-based	cell-based	Quantitative high throughput screen for delayed death inhibitors of the malarial parasite plastid, 96 hour incubation	cell-based	in protocol: “… Four microliters of infected erythrocytes …”	Expert agrees with LLM majority: ChEMBL label incorrect (LLMs agree with BARD)

Appendix

Table 13 : Assays sent for expert review, with ChEMBL label, top LLM prediction, and expert annotation.

Model	Prediction	Correct?	Cited evidence
Claude	organism-based	✓	“…parasites cultured in the presence of test compounds…measuring susceptibility of the living malarial parasite organism.”
Gemini	organism-based	✓	“The susceptibility of the Dd2 Plasmodium falciparum line against novel small molecules will be determined using a SYBR green-based fluorescence assay.”
Gemma 4 31B	organism-based	✓	“The susceptibility of the Dd2 Plasmodium falciparum line…Parasites are cultured in the presence of serial dilutions of test compounds.”
Gemma 3 27B	cell-based	✗	“A cell-based HTS for delayed death inhibitors of the malarial parasite plastid…Parasites are cultured in the presence of serial dilutions of test compounds…”
GPT-OSS 20B	cell-based	✗	“Parasites are cultured in the presence of serial dilutions of test compounds…using a SYBR green-based fluorescence assay in 384-well plates”
GPT-4o	cell-based	✗	“…explicitly indicates the use of living cells (parasites) in the experimental setup.”

Appendix

Table 14: Model predictions for AID 504631, illustrating the cell-based/organism-based confusion for single-celled eukaryotic organisms. True label: organism-based format .

AID	BAO label (third-party)	Majority pred.	Bioassay annotation (PubChem)	Comment	$n_{\text{maj.}}$ / $n_{\text{al.}}$
588725	spectrophotometry	radiometry	scintillation counting (part of radiometry)	LLMs agree with BARD	7/0
588768	fluorescence	spectrophotometry	absorbance (part of spectrophotometry)	LLMs agree with BARD	7/0
588782	fluorescence	luminescence	luminescence	LLMs agree with BARD	7/0
602192	fluorescence	luminescence	chemiluminescence (part of luminescence)	LLMs agree with BARD	7/0
602194	fluorescence	luminescence	chemiluminescence (part of luminescence)	LLMs agree with BARD	7/0
602231	spectrophotometry	isometric tension recording	label-free method (BARD)	LLMs disagree with all sources	7/0

Appendix

Table 15: Manual inspection of the 34 physical detection method assays where the majority of models disagreed with the third-party BAO detection label in the PubChem bulk download. All labels are BAO physical detection methods; the “ method” suffix is omitted for space.

n_{\text{maj.}}

is the number of models supporting the majority prediction;

n_{\text{al.}}

is the number of predictions that aligned with the third-party BAO label instead. Seven models were attempted per row unless noted otherwise. Rows shaded in gray were additionally reviewed by a senior industrial curator (see Table 16 for per-model evidence spans).

AID	3rd-party	Majority pred.	Short title	Expert format	Expert reason
623968	spectrophotometry	fluorescence	Inhibitors of the Hepatitis C Virus non-structural protein 3 helicase (NS3)	spectrophotometry	Similar work to the CHRM probe-development assays but “late stage,” so more information is given, e.g. “RNA concentration is determined by reading absorbance at 260 nm”
588778	fluorescence	spectrophotometry	A High Throughput Screening Assay for Inhibitors of Bacterial Motility in Vibrio cholerae	fluorescence (spectroscopy also fine after seeing LLM evidence)	Protocol: “…fluorescence intensity was determined by reading…”
602418	luminescence	fluorescence	Summary of probe development efforts to identify inverse agonists of LRH-1 (NR5A2)	cannot define	A summary of various efforts per the title; no information on what was actually done for each effort
588816	fluorescence	simple measurement	Identify agonists of the human cholinergic receptor, muscarinic 1 (CHRM1)	cannot be defined	No information; this is pre-work to identify a probe, not really an assay in itself
602170	spectrophotometry	visual observation	Probe development efforts to identify activators of Methionine sulfoxide reductase A (MsrA)	cannot be defined	As above (probe-development pre-work)
624103	fluorescence	simple measurement	Identify agonists of the human cholinergic receptor, muscarinic 5 (CHRM5)	cannot be defined	As above

Appendix

Table 16: Detection-method assays sent for expert review, with third-party (PubChem) label, LLM majority prediction, and expert annotation.

AID	3rd-Party Label	Expert Label	Model	Prediction	Conf.	Evidence Span
588816	fluorescence method	cannot be defined	Llama 3.3 70B	simple measurement ✗	low	No specific detection method mentioned
			GPT-4o	visual observation ✗	low	No specific detection method or instrumentation mentioned.
			Claude Sonnet 4.6	ERROR ✗
			Gemini 3.6 Flash	fluorescence ✗	low	Title indicates probe development for human muscarinic receptor 1 (CHRM1), typically fluorescence-based…
			Gemma 3 27B	simple measurement ✗	low	No information provided. Conservative assumption…
			Gemma 4 31B	label free ✗	low	probe development efforts to identify agonists of CHRM1

Appendix

Table 17 : Detection technology predictions for selected AIDs across all models.

Panel of Figure 6 — Figure 6 : Error rates across predicted confidence levels for silver-label predictions. The top panel shows results for the ChEMBL baseline, while the bottom panel shows the corresponding results for the detection task. Each bar represents the proportion of incorrect predictions within a confidence category, with the number of samples shown in parentheses.

Figure 7 : Effect of two prompt perturbations on proprietary-LLM predictions for the ChEMBL assay format subset. Removing the BAO definitions (Without defs) changes far more predictions than reversing the class order (Order swap), and disproportionately so for Gemini 3.6 Flash (10.8%). Bar segments indicate whether each changed prediction moved from a different label to the silver label (green), from the silver label to another label (red), or between two labels that are different from the silver label (orange); percentages above each bar give the total fraction of predictions that changed.

Experiment	Model	$N$	Parse	Input	Output	Cost	Energy
			fails	tokens	tokens	($)	(kWh)
ChEMBL baseline	Claude Sonnet 4.6	1097	29	2,302,201	90,590	9.09	–
	Gemini 3.6 Flash	1097	0	2,040,815	54,349	3.47	–
	GPT-4o	1097	0	1,998,562	67,554	5.67	–
	Gemma 3 27B	1097	0	2,081,404	64,558	–	6.44
	Gemma 4 31B	1097	0	2,086,917	63,083	–	6.45

Appendix

Table 18: Per-model token usage, parse failures, and cost/energy accounting across all experimental conditions. Input and completion token counts are experiment-wide totals. Cost is reported for API-accessed (proprietary) models; energy for self-hosted (open-weight) models. “–” indicates not applicable.

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