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Principal Scientist CADD


Braine L'alleud, Walloon Brabant, Belgium

Job Function:  Research & Early Development
Job ID:  65600

Principal Scientist, Computer-Aided Drug Design (CADD)

We are seeking an experienced computational chemist to join our Computer-Aided Drug Design (CADD) team in Braine-l'Alleud (Belgium).


The Organization
UCB is a fast-moving global biopharmaceutical company focused on the discovery and development of innovative medicines and solutions to transform the lives of people living with severe diseases affecting the immune and central nervous systems. With more than 7,500 people in approximately 40 countries, the company generated revenue of € 4.6 billion in 2018. UCB invests more than 20% of revenue in cutting-edge scientific research with the objective of solving unmet patient needs. We have a passionate, long-term commitment to the discovery and development of innovative medicines that transform the lives of people with severe diseases. At UCB everything we do starts with a simple question: “How will this make a difference to the lives of people living with severe diseases?” We do that by connecting with patients and their families around the world living with the physical and social burdens of severe disease of the immune system and the central nervous system. Those connections offer new perspectives, drive innovation, and offer the hope of a new generation of therapies that will help to transform lives.


Role Overview

The successful candidate is a scientific expert with proven track record of solving drug design challenges using computational & medicinal chemistry tools. This role gives you an opportunity to demonstrate excellent scientific judgement and to drive the advancement of drug discovery projects to make a positive impact on patients’ lives. This is a hands-on discovery research position in a CADD group with a history of delivery & innovation. You will have in-depth knowledge of various computational approaches (e.g., structure-based, ligand-based, machine learning/AI, etc.) with demonstrable expertise in the domain of predictive modelling. You will support project teams from hit/lead identification through lead optimization and ultimately drug candidate selection. Working with project teams presents development opportunities for you to expand your domain of therapeutic areas. You will work collaboratively with project teams on multiple research sites and across disciplines (e.g., medicinal chemistry, biology, DMPK, structural biology/biophysics) to design compounds with improved properties: potency, off-target profile, ADMET properties, safety profile, etc. This will give you the opportunity to identify strategic collaborations & the right training to enable you to discover new avenues of UCB drug discovery across a variety of exciting therapeutic projects. Additionally, you will be accountable for devising, championing or developing state-of-the-art computational techniques that solve key problems for drug discovery teams. The role provides scope for you to make a broader influence across the UCB organization. You will use your exceptional skills in predictive modeling, excellent communication and team working abilities toward driving innovative compound design.


Primary roles and responsibilities

  • Able to define, implement & influence strategy for discovery projects via deep knowledge of project data and its interpretation. You will demonstrate a clear understanding of project issues through in-depth data analysis and scientific insights.
  • You will critically interpret and propose compound designs to address SAR by incorporating physicochemical properties, pharmacological function readouts, DMPK & safety liabilities.
  • You will analyse project data, recognize aberrant results, and propose explanations including plans for follow-up experiments. Also, proactively suggest experiments to aid further understanding of project data results.
  • Using structure-based, ligand-based, machine learning/AI and other statistical modelling approaches, you will work in drug discovery projects to curate and analyse complex datasets and deliver solutions to address key chemical/biological questions.
  • Broad knowledge of and experience in challenges facing small molecule discovery across multiple modalities. This also presents an opportunity for you to learn beyond NCE modality.
  • Represents CADD in project teams. Collaborates effectively with internal and external partners to deliver on project milestones. Strives to achieve efficient CADD resource use and requests/adjusts according to project needs and priorities.
  • Able to plan, write and publish high quality scientific papers and present work both internally and externally.


Candidate requirements and qualifications

  • You have a PhD, or equivalent, in chemistry, computer science, statistics or other life sciences disciplines, and at least 5 years applying your skills within the pharmaceutical industry.
  • You can demonstrate a deep understanding of the physical chemistry of intra- and inter-molecular interactions in protein-ligand complexes.
  • Hands-on experience in applying structure-based, ligand-based, machine learning/AI technologies to address challenges in chemistry is highly desired.
  • Examples of demonstrated impact on small molecule drug discovery is highly desired.
  • You have working knowledge of issues within drug design related to physicochemical properties, ADME, safety & formulation.
  • Expertise in using and improving computational software; strong scientific computing and programming skills (e.g. Python, C++, Java).
  • Strong written and oral communication skills, and a track record in publishing work in peer-reviewed journals.
  • Ability to effectively interpret and communicate experimental results using advanced data analytics is a must.
  • Experience with large scale data processing, integration and meta-analysis is preferred.


Interested individuals should apply online on UCB website and include the following items:

1. A cover letter outlining research interests and experience as well as career goals

2. Curriculum Vitae

3. Expected availability


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