Donna S. Macmillan

553 total citations
21 papers, 410 citations indexed

About

Donna S. Macmillan is a scholar working on Small Animals, Dermatology and Computational Theory and Mathematics. According to data from OpenAlex, Donna S. Macmillan has authored 21 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Small Animals, 8 papers in Dermatology and 5 papers in Computational Theory and Mathematics. Recurrent topics in Donna S. Macmillan's work include Animal testing and alternatives (12 papers), Contact Dermatitis and Allergies (8 papers) and Computational Drug Discovery Methods (5 papers). Donna S. Macmillan is often cited by papers focused on Animal testing and alternatives (12 papers), Contact Dermatitis and Allergies (8 papers) and Computational Drug Discovery Methods (5 papers). Donna S. Macmillan collaborates with scholars based in United Kingdom, United States and Switzerland. Donna S. Macmillan's co-authors include Craig Jamieson, Helen F. Sneddon, Jane Murray, Allan J. B. Watson, Steven J. Canipa, Richard V. Williams, Chris Barber, Jedd Hillegass, Devin O’Brien and Robert S. Foster and has published in prestigious journals such as International Journal of Molecular Sciences, Green Chemistry and Environmental Toxicology and Chemistry.

In The Last Decade

Donna S. Macmillan

20 papers receiving 373 citations

Peers

Donna S. Macmillan

DERMA

Stefan Kotov Bulgaria

A. Koutsodimou Greece

Sandy Weiner United States

Gerhard Brunner Switzerland

Jacob Krüse Ireland

J.J. Hostýnek United States

V.R. Holland United Kingdom

Denis M. Kuznetsov Russia

Tatjana Gazivoda Kraljević Croatia

Branko J. Drakulić Serbia

Stefan Kotov Bulgaria

Donna S. Macmillan

87 ×0.7

40 ×0.3

22 ×0.2

20 ×0.2

DERMA

26 ×0.4

Citations per year, relative to Donna S. Macmillan Donna S. Macmillan (= 1×) peers Stefan Kotov

Countries citing papers authored by Donna S. Macmillan

Since Specialization

Citations

This map shows the geographic impact of Donna S. Macmillan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Donna S. Macmillan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donna S. Macmillan more than expected).

Fields of papers citing papers by Donna S. Macmillan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Donna S. Macmillan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Donna S. Macmillan. The network helps show where Donna S. Macmillan may publish in the future.

Co-authorship network of co-authors of Donna S. Macmillan

This figure shows the co-authorship network connecting the top 25 collaborators of Donna S. Macmillan. A scholar is included among the top collaborators of Donna S. Macmillan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Donna S. Macmillan. Donna S. Macmillan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Jones, T. D., et al.. (2025). Evaluating the ability of defined approaches to predict the human skin sensitisation potential of chemicals previously untested in new approach methodologies. Regulatory Toxicology and Pharmacology. 164. 105963–105963.

Macmillan, Donna S., Pravin Ambure, V. Aranda, et al.. (2025). Addressing the challenges of acute fish toxicity hazard classification using a non-animal defined approach. Environmental Toxicology and Chemistry. 44(9). 2659–2672. 4 indexed citations

Serrano‐Candelas, Eva, et al.. (2024). GenoITS: Implementation of an Integrated Testing Strategy workflow for genotoxicity using QSAR-based tools. 1. 100005–100005. 1 indexed citations

Högberg, Helena T., Katya Tsaioun, Natàlia García‐Reyero, et al.. (2024). A systematic scoping review of the neurological effects of COVID-19. NeuroToxicology. 103. 16–26. 1 indexed citations

Macmillan, Donna S., Graham Ellis, Roberto Ferro, et al.. (2023). The last resort requirement under REACH: From principle to practice. Regulatory Toxicology and Pharmacology. 147. 105557–105557. 6 indexed citations

Macmillan, Donna S., et al.. (2022). How to resolve inconclusive predictions from defined approaches for skin sensitisation in OECD Guideline No. 497. Regulatory Toxicology and Pharmacology. 135. 105248–105248. 10 indexed citations

Macmillan, Donna S., et al.. (2022). REACHing for solutions: Essential revisions to the EU chemicals regulation to modernise safety assessment. Regulatory Toxicology and Pharmacology. 136. 105278–105278. 9 indexed citations

Macmillan, Donna S., et al.. (2022). Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing. Regulatory Toxicology and Pharmacology. 137. 105292–105292. 3 indexed citations

Api, A.M., Robert S. Foster, G. Frank Gerberick, et al.. (2022). Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system. Regulatory Toxicology and Pharmacology. 133. 105200–105200. 12 indexed citations

10.

Ponting, David J., et al.. (2022). Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity. Methods in molecular biology. 2425. 435–478. 12 indexed citations

11.

Macmillan, Donna S., Abedawn I. Khalaf, Kirsten Gillingwater, et al.. (2022). Selective Anti-Leishmanial Strathclyde Minor Groove Binders Using an N-Oxide Tail-Group Modification. International Journal of Molecular Sciences. 23(19). 11912–11912. 3 indexed citations

12.

Ball, Thomas, Chris Barber, Robert S. Foster, et al.. (2020). Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge. Toxicology Research. 10(1). 102–122. 10 indexed citations

13.

Macmillan, Donna S., Thomas Steger‐Hartmann, Jedd Hillegass, et al.. (2018). Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach. Regulatory Toxicology and Pharmacology. 95. 227–235. 19 indexed citations

14.

Macmillan, Donna S., et al.. (2018). A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria. Regulatory Toxicology and Pharmacology. 101. 35–47. 25 indexed citations

15.

Canipa, Steven J., Donna S. Macmillan, Jeffrey Plante, et al.. (2017). A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert‐derived structure–activity alert spaces. Journal of Applied Toxicology. 37(8). 985–995. 24 indexed citations

16.

Macmillan, Donna S., et al.. (2016). Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays. Regulatory Toxicology and Pharmacology. 76. 30–38. 29 indexed citations

17.

Macmillan, Donna S., et al.. (2013). Development of a solvent selection guide for aldehyde-based direct reductive amination processes. Green Chemistry. 15(5). 1159–1159. 56 indexed citations

18.

Macmillan, Donna S., Jane Murray, Helen F. Sneddon, Craig Jamieson, & Allan J. B. Watson. (2012). Evaluation of alternative solvents in common amide coupling reactions: replacement of dichloromethane and N,N-dimethylformamide. Green Chemistry. 15(3). 596–596. 121 indexed citations

19.

Stevenson, Ross, Robert J. Stokes, Donna S. Macmillan, et al.. (2009). In situ detection of pterins by SERS. The Analyst. 134(8). 1561–1561. 21 indexed citations

20.

Macmillan, Donna S. & Sang Moon Lee. (2006). Organocatalytic Epoxidation Using Hypervalent Iodine Reagents. Synfacts. 2006(11). 1171–1171. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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