Industrial Track CDT studentship with Exscientia

We are recruiting to a fully funded 4-year studentship in collaboration with Exscientia to start in September 2021 as part of our CDT industrial track.

ML-Score: Exploiting machine-learnt potential functions for drug design

Supervisors
Project description
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Modern drug design is a time consuming and costly process that often takes up to 10 years from identifying a drug target to, finding a drug candidate, and getting this candidate approved for clinical use [1]. One way of speeding up this process is in the early stages of drug discovery, where we can make use of computational methods to come up with new drug candidate molecules and computationally predict how well they may bind and inhibit the function of the target protein. Only the most promising molecules are then synthesised reducing time and cost spent on synthesis. As a result, having reliable and accurate methods that can predict how well a drug-like molecule will bind to a target protein at a large scale (more than 1 million molecules) is crucial in speeding up the overall drug discovery process.

Many different approaches have been taken to address this problem from different docking and scoring approaches [2] to dynamics-based approaches such as MMPBSA [3] or alchemical free energy methods [4]. It is broadly acknowledged, that alchemical free energy-based methods are most accurate, with binding affinity prediction accuracy in the range of 1 kcal/mol. Yet they are very computationally costly [4] and can therefore only be used on a small subset of potential molecules. While docking and scoring methods can be used at a large scale, but often the docking score shows little to no correlation to experimentally measured binding affinities. One reason for these shortcomings can be attributed to the scoring function in terms of docking and the force field in terms of the alchemical free energy methods. The project will explore ways in which machine learning and more accurate quantum mechanics-based calculations can be leveraged to improve large scale binding affinity predictions.

Recent advances in machine learning meant that models can be used to learn potential energies from large datasets of density functional theory calculations of drug-like molecules. This means quantum level accuracy for predicting interaction energies from such machine-learnt potentials can be reached at a computational relevant for pharmaceutical timescales [5]. There has been an initial proof of concept studies to show how these methods can be used to improve accuracies of alchemical free energy calculations with errors in comparison to experiment being as low as 0.5 kcal/mol [6]. The idea behind this project is to explore the utility of these methods in the context of different affinity prediction strategies such as ensemble-based docking, as well as MD based methods. The goal will be to reach high accuracies at drastically reduced computational cost, providing a fast and accurate computational affinity prediction method in the early stages of drug design.

Applicant profile

Applicants need to have a UK 2.1 honours degree, or its international equivalent, in a science discipline and interested in using mathematical, machine learning-based, and computational tools to address biomolecular problems.

Funding Eligibility

The studentship funding is only available for Home (UK) status applicants: UK nationals and EU/EEA/Swiss nationals holding settled or pre-settled status in the UK. To work out your fee status see guidance here.

CDT studentships fund 4 years of study, covering tuition fees, stipend (£15,285 in 2020/21) and travel/research support.

CDT Programme

The CDT programme follows 1+3 format. In Year 1 you will study towards a Master by Research, undertaking a number of taught courses and taster research projects to broaden and refine your skills and explore different research areas. Following that you will begin your PhD project “ML-Score: Exploiting machine-learnt potential functions for drug design” co-supervised between the University of Edinburgh and Exscientia. For more information see:

Apply

Application is open now for admission in September 2021 as part of the main CDT cohort.  For fullest consideration, applications should be submitted by 16:00 on  18 June 2021.

Application form

You will need to submit the following documents with your application. Make sure these are obtained in good time as we cannot consider applications without them.

  • Personal statement explaining why you want to be considered for the programme and what you think your major strengths are. Make sure you highlight any unique aspects or experiences that you think are relevant to your application that do not appear in your CV.
  • CV which includes your educational history, work experience and any relevant research publications, and highlights any special achievements.
  • Research proposal Describe a novel research project relevant to the aims of the CDT of your own design with reference to emerging challenges and methodologies in the current literature. The proposal should answer the following questions: What is the challenge? Why is this important and timely? How do you propose to tackle it (you could include specific technical details here)? What is the current state of the relevant research in this area and what do you propose that is novel? What are the potential societal implications of your proposed research? The research proposal is an important component in our assessment of a candidate’s suitability and aptitude for the CDT programme.  Proposals should be 1-2 A4 pages in length in a 10pt font exclusive of references.
  • Degree certificate and transcript for both undergraduate and postgraduate studies, if applicable. If your studies are in progress, you will be asked to upload an interim transcript, otherwise your application cannot be considered.
  • Proof of English language proficiency. If you don’t have an English language certificate yet, we will still consider your application. However if an offer of admission is made, it will be conditional on you providing an English Language certificate which does meets University requirement.
  • 2 reference letters.