ProfessorsProfessors of University of LeedsRichard Marchese Robinson

Richard Marchese Robinson

University of Leeds

H-index: 15

Europe-United Kingdom

About Richard Marchese Robinson

Richard Marchese Robinson, With an exceptional h-index of 15 and a recent h-index of 14 (since 2020), a distinguished researcher at University of Leeds, specializes in the field of Computational chemistry, QPSR/QSAR, predictive toxicology, Machine Learning, nanoinformatics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish

A cross-industry collaboration to assess if acute oral toxicity (Q) SAR models are fit-for-purpose for GHS classification and labelling

Richard Marchese Robinson Information

University	University of Leeds
Position	___
Citations(all)	1256
Citations(since 2020)	864
Cited By	758
hIndex(all)	15
hIndex(since 2020)	14
i10Index(all)	19
i10Index(since 2020)	17
Email	Access Email
University Profile Page	University of Leeds
Google Scholar	View Google Scholar Profile

Richard Marchese Robinson Skills & Research Interests

Computational chemistry

QPSR/QSAR

predictive toxicology

Machine Learning

nanoinformatics

Top articles of Richard Marchese Robinson

Title	Journal	Author(s)	Publication Date
Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning	Beilstein Journal of Nanotechnology	Richard Liam Marchese Robinson Haralambos Sarimveis Philip Doganis Xiaodong Jia Marianna Kotzabasaki ...	2021/11/29
Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish	Nanotoxicology	C Gousiadou RL Marchese Robinson M Kotzabasaki P Doganis TA Wilkins ...	2021/4/21
A cross-industry collaboration to assess if acute oral toxicity (Q) SAR models are fit-for-purpose for GHS classification and labelling	Regulatory Toxicology and Pharmacology	Joel Bercu Melisa J Masuda‐Herrera Alejandra Trejo-Martin Catrin Hasselgren Jean Lord ...	2020/12/17