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Advice for bad computational toxicologists
Srijit Seal1,2, Richard R Rabbit Aka Thomas Hartung3,4
1Broad Institute of MIT and Harvard, Cambridge, Massacheusetts, US.
NAM Journal
|June 29, 2026
Summary
This study offers guidance for computational toxicologists, highlighting common pitfalls in the field. It contrasts this with advice previously given to traditional toxicologists to improve research practices.
Area of Science:
- Toxicology
- Computational Biology
- Scientific Research
Background:
- Traditional toxicology practices have established methods.
- Emerging computational toxicology presents new challenges.
- Ensuring quality in toxicological research is paramount.
Purpose of the Study:
- To provide specific advice for computational toxicologists.
- To identify potential errors in computational toxicology workflows.
- To complement existing guidance for traditional toxicologists.
Main Methods:
- Review of common errors in computational toxicology.
- Comparative analysis of traditional and computational toxicology approaches.
- Guidance formulation based on identified shortcomings.
Main Results:
- Numerous potential pitfalls exist in computational toxicology.
- Specific examples of suboptimal practices are identified.
- Guidance is tailored to the unique aspects of computational toxicology.
Conclusions:
- Adherence to best practices is crucial for reliable computational toxicology.
- Continuous evaluation of methods is necessary in this evolving field.
- This work aims to improve the quality and integrity of toxicological studies.
