CNS Drug Structure–Activity Relationship Reasoning Q&A Dataset

#structure–activity relationship reasoning #molecular analogue comparison #experimental property tradeoff analysis #CNS drug discovery #structure–activity relationship analysis #small-molecule lead optimization
  • 3960
  • 0.4GB
  • JSON
  • CATL
  • MOBIUSI INCMOBIUSI INC
Updated:2026-11-05

AI Analysis & Value Prop

Designed for central nervous system small-molecule drug discovery, this dataset presents text-based structural differences across compound series alongside experimental results for activity, selectivity, solubility, and related properties. Questions cover analogue comparison, identification of supporting evidence and counterexamples for structure–activity relationships, and analysis of tradeoffs among properties, with concise reasoning answers. It supports training and evaluation of logical reasoning and evidence assessment in medicinal chemistry.

Dataset Insights

Sample Examples

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Technical Specifications

FieldTypeDescription
file_namestringFile name
answer_textstringConcise answer to the question, including the comparison or conclusion supported by the source context.
source_textstringOriginal text containing the medicinal chemistry question and relevant analogue structures, experimental results, and other context.
reasoning_textstringBrief explanation connecting structural differences to observed activity, selectivity, solubility, or other reported properties.
counterevidencestringSummary of relevant observations that qualify or contradict the proposed structure–activity relationship.
property_tradeoffsstringExplanation of tradeoffs among activity, selectivity, solubility, or other properties discussed in the answer.
supporting_evidencestringSummary of experimental observations that support the stated structure–activity relationship.

Compliance Statement

Authorization TypeProprietary - Commercial AI Training License (No Redistribution)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?
It covers CNS small-molecule series and provides text-based structural differences, experimental information such as activity, selectivity, and solubility, and related questions, concise answers, and reasoning. Some records also include supporting evidence, counterevidence, and property tradeoff analysis.
Which capabilities can it support for training or evaluation?
The dataset can support structure–activity reasoning, medicinal chemistry analogue comparison, assessment of experimental evidence, and analysis of tradeoffs among multiple properties. It can be used for reasoning-oriented supervised fine-tuning or to evaluate whether a model draws careful conclusions from the information provided.
How is the data presented?
The data is presented as question-and-answer text with concise reasoning. Available fields may include the question context, answer, reasoning, supporting evidence, counterevidence, and property tradeoff explanation. Check the product listing for the specific file format, record count, and delivery method.
How can I obtain the dataset, and what are the licensing terms?
Check the product listing for available access methods, license terms, and restrictions on commercial use. Before use, confirm that the permitted scope covers your intended activities, including model training, evaluation, or redistribution.

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Cite this Work

@dataset{Mobiusi2026,
  title={CNS Drug Structure–Activity Relationship Reasoning Q&A Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/1afa1f8b820a3ca37777ca646a7ab572},
  urldate={2026-10-22},
  keywords={CNS drug dataset,drug SAR Q&A,small molecule drug reasoning,medicinal chemistry data,analogue comparison,structure activity analysis,drug property tradeoffs,reasoning training data},
  version={1.0}
}

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