Working at Almac Discovery

If you are passionate about your science and working in a driven environment dedicated to oncology and would welcome the opportunity of a friendly but challenging company we are always interested in hearing from you.

Please email us at recruit@almacgroup.com

You can view a full list of current Almac Discovery vacancies and wider Almac Group positions at Almac Careers.

 

Case Studies – Our People

Hugues 

I have contributed to a wide range of projects ranging from hit finding, hit to lead and lead optimzation. Having a keen interest  in exploring new chemical space around bioactive molecules, I find that every project we work on offers multiple opportunities to develop one’s own creativity. It always surprises me to find relatively simple molecules of interest can be unknown in the literature with no straightforward synthetic routes.

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Chemistry is of course a means to an end but sometimes, it can allow a small but experienced Biotech Company  such as Almac Discovery to differentiate itself from other companies by making novel compounds to targets that other may judge as intractable.

Designing new analogs involves making critical choices taking into account the feedback of a multidisciplinary team (computational, biology and ADME) and last but not least, a key parameter : time !

 

Mark

Working for Almac Discovery has been an exciting experience so far.  I joined in March 2015 at around the same time that the company was partnering a significant deubiquitinase oncology target.  I have found the passion, energy and enthusiasm of the scientists here infectious and something that should drive the future success of the company.

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I’m passionate about Cancer Research, in particular how the changes in human genomes drive cancer.  We still have so much to learn – for example only 2% of the human genome is made up of protein coding genes; examining the role the other 98% plays, may help us improve our understanding of this disease.

My role as a computational biologist are to help drive the discovery of new oncology targets by utilizing the plethora of large scale cancer genome data available publicly and in-house, and working with our talented biologists and chemists to facilitate the development of our existing portfolio of projects.

 

Oliver

Perhaps counter-intuitively for a scientist, my primary role is to minimize the number of experiments that we perform.  As a lean and agile organization, it is important to target our resources as efficiently as possible, and that is where computational chemistry comes in.  I use whatever information we have regarding our disease targets and compounds to create predictive models that allow us to prioritize the most promising molecules for testing.

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Computational chemistry is a broad field, reflected by the wide range of data that can be employed.  A 3D crystal structure of the protein target is ideal, allowing for the use of methods such as docking, in which prospective compounds are placed in the active site in various orientations and conformations, where they are ranked using an energy function.  If a crystal structure is not available, it may be possible to construct a model based on a similar protein and use it in the same manner.  Alternatively, there are many approaches that do not require a protein structure; a lot can be learnt from analyzing the preferred conformations of a molecule, for example, and the smaller system size allows for the use of more complex quantum mechanical calculations to enhance accuracy.  If a sufficient quantity of assay data is available, a statistical model can be created that relates molecular features and/or properties to experimental data and used to predict results for novel compounds.

Use of these approaches – either singly or in combination – allows for the in-silico testing of far more molecules than can be experimentally assayed in the same amount of time.  While accuracy is not perfect, prioritizing compounds in this way allows us to optimize a compound series in far fewer iterations than if we relied only on experimental methods.