Computational Chemistry & Molecular Design
Basic Qualifications / Minimum Requirements:
- MS, PhD or equivalent with relevant experience in a Computational Science
- Evidence of a broad knowledge of computational sciences which will include a deep knowledge of one or more areas of machine learning
- Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in the Life Sciences
- Demonstrated ability to build, apply, and evaluate the results of robust statistical models from large scientific data sets
- Demonstrated ability to utilize computer programming and scripting languages such as R, C/C++, Python or Java.
- Evidence of strong critical thinking skills
- Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial, collaborative attitude
Preferred Qualifications / Desired Skills/Qualifications:
- Demonstrated skills in building Quantitative Structure Activity Relationship (QSAR) models.
- Demonstrated skills to programmatically collect, combine, mine and analyze complex biological and chemical data
- Experience with chemical toolkits such as ChemAxon, OEChem or RDkit
- Experience with scientific pipelining tools such as Pipeline Pilot or Knime.
- Knowledge of medicinal chemistry
The Computational and Modeling Sciences Department (CMS) generates and applies computational models across the drug discovery pipeline from lead discovery to late stage development. As part of CMS, the Molecular Design group works closely with multidisciplinary drug discovery teams, applying computational approaches combined with knowledge of chemistry, biology, and the drug discovery/development process to facilitate the identification, prioritization and progression of bioactive agents. We have a need for a scientist with deep expertise in modern machine learning techniques, informatics, and their application to the discovery of medicines.
Our scientists are responsible for generating testable hypotheses that drive discovery programs, extracting and analyzing decision-making data associated with molecular structure and building predictive models based on such data. The successful candidate will work collaboratively with other team members to ensure that work is aligned with strategic priorities, and seek out new opportunities for impact of modeling methodologies on drug discovery at GSK.
- Use machine learning to build predictive models for application in drug discovery programs
- Develop automated capabilities which enable the application of machine learning across the discovery portfolio, from small molecules to biopharmaceuticals.
- Be an expert resource and consultant for other members of the Molecular Design department
- Prepare and present results to internal and external groups
- Work with others within a matrix organization
- LI GSK*
You may apply for this position online by selecting the Apply now button.
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