README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - chemistry
  - solubility
  - cheminformatics
  - llm-benchmark
  - smiles
pretty_name: SoluBench
configs:
  - config_name: task1
    data_files:
      - split: test
        path: task1_pairwise_solvent_comparison.csv
  - config_name: task2
    data_files:
      - split: test
        path: task2_best_solvent_selection.csv
  - config_name: task3
    data_files:
      - split: test
        path: task3_cosolvent_effect_prediction.csv
  - config_name: task4
    data_files:
      - split: test
        path: task4_pairwise_compound_comparison.csv

SoluBench

SoluBench is a benchmark for evaluating large language models on solubility-related tasks of various complexity. It is built on top of BigSolDB v2.0 and MixtureSolDB — two curated experimental solubility datasets.

📄 Preprint: coming soon
💻 GitHub: levakrasnovs/SoluBench

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Tasks

Config	Task	Description	Input	Output	n	Random baseline
task1	Pairwise solvent comparison	Given a compound (SMILES) and two solvents, select the one with higher solubility	SMILES + 2 solvents	A / B	3699	50.0%
task2	Best solvent selection	Given a compound and a list of solvents, select the one with highest solubility	SMILES + N solvents	letter	—	~17.1%
task3	Co-solvent effect prediction	Given a compound, a base solvent and a co-solvent mixture, predict whether solubility increases or decreases	SMILES + mixture composition	A / B	—	~16.7%
task4	Pairwise compound comparison	Given a solvent and two compounds (SMILES), select the one with higher solubility	Solvent + 2 SMILES	A / B	3000	50.0%

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Usage

from datasets import load_dataset

# Load a specific task
ds = load_dataset("levakrasnovs/SoluBench", "task1")
print(ds["test"][0])

# Load all tasks
for task in ["task1", "task2", "task3", "task4"]:
    ds = load_dataset("levakrasnovs/SoluBench", task)
    print(f"{task}: {len(ds['test'])} rows")

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Data fields

Task 1 — Pairwise solvent comparison

Field	Description
id	Unique row identifier
DOI	Source publication
SMILES	Compound SMILES
Temperature_K	Temperature in Kelvin
Solvent_A	First solvent
Solvent_B	Second solvent
LogS_A	Experimental log solubility in solvent A (mol/L)
LogS_B	Experimental log solubility in solvent B (mol/L)
Delta_LogS	|LogS_A − LogS_B|
Answer	Correct answer (A or B)

Task 2 — Best solvent selection

Field	Description
id	Unique row identifier
DOI	Source publication
SMILES	Compound SMILES
Temperature_K	Temperature in Kelvin
Best_solvent	Solvent with highest solubility
Best_LogS	LogS in best solvent
Second_best_solvent	Solvent with second highest solubility
Second_best_LogS	LogS in second best solvent
Delta_LogS_best_minus_second	LogS difference between best and second best
All_solvents	Semicolon-separated list of all solvents
All_LogS	Semicolon-separated list of all LogS values
Answer	Correct answer (letter corresponding to best solvent)

Task 3 — Co-solvent effect prediction

Field	Description
id	Unique row identifier
DOI	Source publication
SMILES	Compound SMILES
Temperature_K	Temperature in Kelvin
Base_solvent	Pure base solvent
Added_solvent	Co-solvent added to the mixture
Fraction_base	Fraction of base solvent
Fraction_added	Fraction of added co-solvent
New_composition	Human-readable mixture description
Fraction_type	Type of fraction (mole/volume/mass)
Delta_LogS_new_minus_base	LogS(mixture) − LogS(pure base)
Answer	A = solubility enhanced, B = solubility reduced

Task 4 — Pairwise compound comparison

Field	Description
id	Unique row identifier
Solvent	Solvent name
Temperature_K	Temperature in Kelvin
SMILES_A	SMILES of compound A
SMILES_B	SMILES of compound B
Compound_name_A	Name of compound A
Compound_name_B	Name of compound B
LogS_A	Experimental log solubility of compound A (mol/L)
LogS_B	Experimental log solubility of compound B (mol/L)
DOI_A	Source publication for compound A
DOI_B	Source publication for compound B
delta_logS	LogS_A − LogS_B
Answer	Correct answer (A or B)

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Source datasets

Dataset	Paper	Download
BigSolDB v2.0	Nature Scientific Data	Zenodo
MixtureSolDB	ChemRxiv	Zenodo

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Model results

Full model evaluation results (20+ LLMs including GPT, Claude, Gemini, Grok, Qwen, DeepSeek, GLM) are available in the GitHub repository.

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Citation

Citation will be added upon preprint publication.

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License

CC BY 4.0