Dennis Pelletier Email & Phone Number | Senior Principal Computational Toxicologist in Pfizer's Drug Safety Research & Development (DSRD), Computational Safety Sciences (CSS) group. Molecular Biology education combined with applied bioinformatics and coding experiences focused on pragmatic solutions to opportunities in preclinical and clinical areas. Custom application design, development, and implementations incorporating workflow automation and analytical consistency with 'easy button' approach for end user simplicity, interpretability, and time savings in biological and drug development big-data space. AI/ML/NLM: Focused on value-adding uses of traditional NLP approaches and newer Large Language Models (LLMs) in pharmaceutical discovery and development. Data extraction from literature and regulatory documents. Crowd-sourced curation and validation. Large, diverse dataset summarizations. Drug target safety assessments via comprehensive integration of target biology, pharmacology, and genetics in support of target selection, species relevance, and derisking activities. R coding skillset, RStudio/Shiny application development, big data integration and analysis. Knack for leading multiline, matrixed teams on challenging projects External partnerships to leverage best of external capabilities for project acceleration and successful outcomes

Dennis Pelletier

Senior Principal Computational Toxicologist in Pfizer's Drug Safety Research & Development (DSRD), C...
Gales Ferry, Connecticut, United States

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Dennis Pelletier’s Location Gales Ferry, Connecticut, United States

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Senior Principal Computational Toxicologist

2019-06-01T00:00:00.000Z - Present

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Dennis Pelletier is a Senior Principal Computational Toxicologist in Pfizer's Drug Safety Research & Development (DSRD), Computational Safety Sciences (CSS) group. Molecular Biology education combined with applied bioinformatics and coding experiences focused on pragmatic solutions to opportunities in preclinical and clinical areas. Custom application design, development, and implementations incorporating workflow automation and analytical consistency with 'easy button' approach for end user simplicity, interpretability, and time savings in biological and drug development big-data space. AI/ML/NLM: Focused on value-adding uses of traditional NLP approaches and newer Large Language Models (LLMs) in pharmaceutical discovery and development. Data extraction from literature and regulatory documents. Crowd-sourced curation and validation. Large, diverse dataset summarizations. Drug target safety assessments via comprehensive integration of target biology, pharmacology, and genetics in support of target selection, species relevance, and derisking activities. R coding skillset, RStudio/Shiny application development, big data integration and analysis. Knack for leading multiline, matrixed teams on challenging projects External partnerships to leverage best of external capabilities for project acceleration and successful outcomes