Job: Computational Chemist at Oxford Drug Design

Oxford Drug Design is a unique biotechnology company. Supported by a proprietary set of computational chemistry methods developed in-house over the past 15 years, we are discovering new antibiotics to address the urgent threat of multi-drug resistant infections. Following success in obtaining funding from Innovate UK, the UK’s Innovation agency, we are expanding our computer-aided drug design (CADD) team.

Your Role:

  • Develop and validate novel CADD methodology in the areas of machine learning, statistical methods, chemogenomics and computational chemistry, working in a multidisciplinary team of internal and external colleagues, and be a key contributor to their success.
  • Use a diverse array of computational technology to devise hypotheses for structure-activity relationships and compounds to test these hypotheses, focused on our antibiotic drug discovery portfolio.
  • Maintain and build our internal cheminformatics databases and search technologies.
  • Maintain awareness of the latest technologies and developments in CADD and project areas.
  • Maintain an internal and external scientific presence by authoring significant scientific presentations and publications. 

Who you are:

You will hold a PhD (or equivalent experience) in computer science, machine learning, computational chemistry, or a related field. Industry experience would be an advantage. You are expected to have a high degree of independence and self-motivation. Ideally, your background would include some of the following:

  • Expertise in machine learning and other statistical approaches to data analysis and model generation.
  • Expertise in scientific computing, and good programming skills (Python, C++ and Java skills are particularly relevant).
  • Expertise in using cheminformatics techniques and relational databases.
  • Knowledge of computational chemistry and medicinal chemistry concepts is an advantage.
  • Very good written and verbal communication skills.

Please note that candidates must be eligible and able to work in the UK. Please include details of your eligibility with your application.

To apply, please send a CV and covering letter by 23 April-2019, quoting reference CC19-1, to: [email protected]

Oxford Drug Design Ltd., Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK Telephone: +44 (0)1865 261469

Announcement: Course on Machine Learning for Chemoinformatics

Dr Martyn Winn would like to announce the following course:

Machine Learning for Chemoinformatics
Location: RAL, Harwell Campus
Dates: 6th – 10th August 2018
Registration fee:  academic £150, commercial £1000

The course covers all of the major methods in machine learning for classification and regression, with examples taken from problems in chemoinformatics. It has been designed for data analysts working in the pharmaceutical industry, but would also be useful to chemists interested in advanced data analysis. The course includes practical examples using commonly available Python packages.

For further information and to register, go to:


Dr. Martyn Winn
STFC Daresbury Laboratory, Daresbury, Warrington, WA4 4AD, U.K.
Tel: +44 1925 603455 (DL)   or   +44 1235 567865 (RcaH)
E-mail: [email protected]       Skype: martyn.winn


TL;DR: CCK-8, the 8th of our new series of meetings for computational chemists, cheminformaticians, and molecular modelers, is on Thursday, October 26th, 2017, at 5 pm in the Seminar Room, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QUFree tickets are available.


Dear Friends and Colleagues,

Please join us for our next “Comp Chem Kitchen”, CCK-8, at 5-6 pm on Thursday, October 26th, 2017, in the Seminar Room, Department of Biochemistry, South Parks Road, Oxford. We are really pleased to announce Prof. Alpha Lee from the Department of Physics, University of Cambridge, UK, will be speaking:

  • Prof. Alpha Lee (Cambridge): Exploring chemical space using random matrix theoryDeveloping computational methods to explore chemical space is a major challenge for drug discovery. The challenge is often the limited number of experimental measurements relative to the vast chemical space. I will discuss a mathematical framework, inspired by random matrix theory, which allows us to remove noise due to finite sampling and identify important chemical features. I will illustrate this framework with three examples: predicting protein-ligand affinity [1], optimal design of experiments by combining coarse and fine measurements [2], and inferring a generative model in chemical space by combining Ornstein-Zernike theory with deep learning [3].[1] A. A. Lee, M. P. Brenner and L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A., 113, 13564 (2016);
    [2] A. A. Lee, M. P. Brenner and L. J. Colwell, Phys. Rev. Lett., accepted (2017) arXiv:1702.06001 [PDF];
    [3] A. A. Lee, arXiv:1706.08466 (2017) [PDF].

Lightning Talks:

  • Simon Nadal (Groups of Prof. Ben Davis, CRL; and Prof. Shabaz Mohammed, Biochemistry): “SILCS simulations in preclinical drug discovery“.
  • Anthony Aimon (Visiting Scientist, OxXChem, i04-1 Beamline, Diamond Light Source): “Opentrons: Automated Organic Synthesis made easy“.

Get in touch if you would like to give a 5 minute talk at a future CCK on your latest research or give a quick demo your latest programming project, or even to nominate someone (students, postdocs, professionals, PIs, Emeritus Professors). The talks usually resemble one of the following styles:

  • an overview of computational chemistry in your research;
  • a (live!) demonstration of some software that you are developing or using; or
  • a summary of a computational chemistry paper, method, programming language, or tool that you’ve seen recently.

Refreshments will be provided, including beer.

We would like to thank the University of Oxford MPLS Network and Interdisciplinary Fund for making CCK possible.

About CCK

Comp Chem Kitchen is a regular forum and seminar series to hear about and discuss computational methods for tackling problems in chemistry, biochemistry and drug discovery. It focuses principally on cheminformatics, computational chemistry, and molecular modelling, and overlaps with neighboring areas such as materials properties and bioinformatics.

We’re keen to encourage people involved in coding and methods development (i.e. hackers, in the original untarnished sense of the word) to join us. Our hope is that we will share best practices, even code snippets and software tools, and avoid re-inventing wheels.

In addition to local researchers, we invite speakers from industry and non-profits from time to time, and occasionally organize software demos and tutorials.

If you’re interested in giving a talk, here are some possible topics:

  • Software development (e.g.: Python, C, C++, CUDA, shell, Matlab);
  • Optimizing force field parameters & EVB models;
  • Cheminformatics (e.g.: RDKit);
  • X-ray and NMR crystallography, including small molecule and macromolecular;
  • Protein & RNA modeling, including Molecular Dynamics;
  • Virtual screening and Docking;
  • Machine Learning;
  • Quantum Methods, including DFT.

Bring your laptops, by the way, if you have something you’d like to show!


Want to speak? Ideas for speakers?

* If you have ideas for speakers, or would like to give a talk, let us know. We also invite lightning talks of 5 minutes (or fewer) from attendees, so if you have some cool code you’ve been working on and would like to demo, bring your laptop, smartphone, tablet, (wearable?) and tell us all about it. *

Please pass this message on to friends, colleagues, and students who may be interested too!

The main CCK web site is:
Follow us on Twitter: @CompChemKitchen
See you soon! We’re looking forward to seeing and hearing about the diverse range of computational molecular science that you’re cooking up…

—Garrett, Richard, Phil and Rob

[email protected]
[email protected]
[email protected]

First meeting

CCK-1 (Tuesday 24th May)

In the spirit of the name, our inaugural meeting, CCK-1 will be held in the Abbott’s Kitchen in the Inorganic Chemistry Laboratory at 5pm on Tuesday May 24th 2016 (5th Week).

Refreshments will be provided.

Jerome Wicker from Chemistry will be speaking about “Machine learning for classification of solid form data extracted from CSD and ZINC”. The software tools discussed include RDKit, CSD, and scikit-learn.

There will also be 2 lightning talks, each ~5 minutes long. Hannah Patel from the Department of Statistics will speak on “Novelty Score: Prioritising compounds that potentially form novel protein-ligand interactions and novel scaffolds using an interaction centric approach”. Software covered will include Django and RDKit. Dr Michael Charlton from InhibOx will also speak on his latest research.