Skylar Connor Data-verified

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

ORISE Post Doctoral Fellow

NCTR
Last publication 2025 Last refreshed 2026-05-16

postdoc

5 h-index 8 pubs 71 cited

Research Areas

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ORCID

OverviewAI-generated summary

Skylar Connor, an ORISE Post Doctoral Fellow at the National Center for Toxicological Research, focuses on the application of artificial intelligence (AI) and computational methods in toxicology and drug safety evaluation. Their work investigates the adaptability of AI for predicting drug-induced injuries, particularly in the liver and kidneys. Connor has published on the development of databases and classification systems, such as DILIrank 2.0 for drug-induced liver injury and DICE for drug indication classification, to facilitate AI-based extraction and safety assessment. Recent publications also explore the generation of renal injury lists to advance new approach methodologies for nephrotoxicity and the readiness of AI tools like ChatGPT for toxicological research. Connor collaborates with researchers at the National Center for Toxicological Research, including Ting Li and Weida Tong, on studies related to drug-induced toxicity and AI applications in regulatory science.

Metrics

  • h-index: 5
  • Publications: 8
  • Citations: 71

Selected Publications

  • DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025)
    DOI OpenAlex
  • Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research? (2025)
    2 citations DOI OpenAlex
  • Drug-induced kidney injury: challenges and opportunities (2024)
    12 citations DOI OpenAlex
  • Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024)
    19 citations DOI OpenAlex
  • Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury (2022)
    16 citations DOI OpenAlex
  • Best practice and reproducible science are required to advance artificial intelligence in real-world applications (2022)
    5 citations DOI OpenAlex
  • DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction (2021)
    9 citations DOI OpenAlex

View all publications on OpenAlex →

Collaboration Network

17 Collaborators 8 Institutions 2 Countries

Top Collaborators

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