University of Leeds Featured PhD Programmes
John Innes Centre Featured PhD Programmes
University of Kent Featured PhD Programmes
Catalysis Hub Featured PhD Programmes
University of Sheffield Featured PhD Programmes

Understanding artificial intelligence models for toxicity prediction

Project Description

The Sheffield Chemoinformatics Research Group is pleased to offer a fully-funded three-year PhD studentship in collaboration with Lhasa Limited. The student will be based at the University of Sheffield’s Information School (ranked best in UK and Europe in its field by the QS University ranking 2019) with an expected placement at Lhasa Limited’s base in Leeds, a world leader in knowledge and data sharing to improve the drug development process.

Project description
Artificial intelligence (AI) models, such as those obtained from deep neural networks (DNN), are seeing increasing usage in the chemoinformatics field. However, so far, applications of DNNs have focused on improving the accuracy of models, and comparing them to state-of-the-art methods. There has been comparatively limited research to understand the fundamental characteristics of the models trained. Thus, DNNs are considered ‘black box’ models because it is difficult to understand how they produce their predictions.

Computational prediction methods are of particular relevance for toxicity prediction. A key factor for the acceptance of predictive models for regulatory approval is the requirement to provide a mechanistic interpretation of the model. This requirement therefore limits the use of AI techniques for toxicity prediction.

In this PhD, the successful applicant will focus on an important aspect of DNN models that has seen little research so far: enhancing the interpretability of the trained models. They will apply different strategies with the aim of understanding which chemical patterns the models learn when they are making toxicity predictions. The results of the project could provide a deeper understanding of the causes of toxicity. The methods developed will be readily transferable to other types of chemical endpoints of interest such as biological activities.

Joint supervisors:
Professor Val Gillet, Professor of Chemoinformatics
Dr Antonio de la Vega de León, Lecturer in Chemoinformatics

This PhD is available to UK and EU students in possession (or expected to possess before the beginning of the PhD) of a merit MSc. Candidates are required to provide evidence of English language ability through either a recent degree from a majority English-speaking country, or a 6.5 overall IELTS score with at least 6.0 in each component. This project is suitable to computer science students with an interest in chemistry and toxicology, as well as chemistry students with an interest in computer science and machine learning.

UK/EU applicants will be eligible for a full award paying fees and maintenance at standard Research Council rates. The stipend RCUK rates for 2019/20 studentships are fees £4,327, stipend £15,009 per annum.

Application process
Prospective applicants should send their CV and two reference letters to or . Pre-selected candidates will be invited to an interview.

Funding Notes

UK/EU applicants will be eligible for a full award paying fees and maintenance at standard Research Council rates. The stipend RCUK rates for 2019/20 studentships are fees £4,327, stipend £15,009 per annum.

How good is research at University of Sheffield in Communication, Cultural and Media Studies, Library and Information Management?

FTE Category A staff submitted: 22.30

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully

FindAPhD. Copyright 2005-2019
All rights reserved.