Principle Scientist - Cheminformatics

Approach to R&D

For candidates seeking to be located at our Stevenage site, this role will temporarily be based ever, the Company plans to relocate its offices to Cambridge, UK. The location of this role will therefore subsequently change to Cambridge UK, in accordance with timelines to be set by the Company. The relocation is currently proposed to take effect by early 2029.

Do you share a desire to advance scientific knowledge and harness the revolution in data, automation and predictive sciences to deliver measurable impacts on the success and progression of GSK's medicine discovery portfolio?

The Data, Automation, and Predictive Sciences (DAPS) function of GSK Research Technologies focuses on large-scale data generation, curation, analysis, and prediction to increase the Probability of Technical and Regulatory Success (PTRS) of assets and unlock upper quartile ambitions.

Collaboration is key, as DAPS will only be successful by working in close partnership with matrix teams within Research Technologies functions, Research Units (all therapeutic areas), R&D Digital & Tech (RDDT), R&D AIML, and Risk & Compliance.

We are seeking a Principal Scientist within the Cheminformatics (CIX) group. You will leverage large proprietary internal datasets to develop, integrate and embed advanced computational methods and predictive in silico models that accelerate the discovery of medicines. With a focus on machine learning, cheminformatics and computational chemistry methodologies, you will help drive the development and delivery of key new capabilities for our internal BRADSHAW automated design platform.

• Build, validate and deploy machine learning models spanning cheminformatics, computational chemistry and quantum mechanics and inform go/no-go decisions across drug discovery programmes.
• Prototype exploratory, agent-driven workflows that combine LLM-based reasoning with cheminformatics tools, predictive models and experimental data to accelerate hypothesis generation, literature and data triage, and iterative design-make-test-analise cycles.
• Integrate with drug discovery programme teams, including DMPK, Toxicology and Safety Pharmacology, to embed predictive models directly into decision-making and translate computational outputs into guidance that is accessible to non-experts.
• Contribute to and validate production-quality code implementing state-of-the-art cheminformatics, computational chemistry and quantum chemistry methods that accelerate and improve decision-making on drug discovery programmes.
• Prepare and present results of key validation experiments, details of capability builds, and developments on active drug discovery projects to internal and external groups in a way that is both informative and accessible to the non-subject matter expert.
• Work with others within a multidisciplinary matrix team that spans different organizations and geographies to execute on joint objectives

• PhD or MSc in Cheminformatics, Computational Chemistry, Informatics, Life Sciences or equivalent with a strong Chemistry foundation.
• Expertise to programmatically collect, combine, mine and analyse complex biological and chemical data to build predictive models.
• Evidence of a broad knowledge of computational sciences including knowledge of machine learning, computational chemistry and cheminformatics methods applied to drug design across differing modalities.
• Knowledge of related disciplines (medicinal chemistry, HT screening, analytical chemistry, systems biology, DMPK, Tox, Imaging) to enable multidisciplinary approaches to be identified and integrated into a cohesive project plan is preferred.
• Evidence of developing and utilizing computer programming and scripting languages such as Python, Java, C/C++, R with knowledge of basic software development practices.
• Expertise with chemical toolkits such as Chem Axon, RDkit and scientific pipelining tools such as Pipeline Pilot, KNIME.
• Evidence of strong critical thinking skills, problem-solving & high learning agility.
• Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial, collaborative attitude.
• Demonstrated ability to work as contributing team member and ability to participate in a matrixed team environment.

• Knowledge of and experience applying DNN to drug discovery including de-novo molecular generation, reaction and retrosynthetic prediction, property prediction
• Experience applying modern experimental design and acquisition strategies to library design and high throughput chemistry including methods such as Bayesian optimisation
• Experience with Quantum Mechanical methods (e.g. DFT, QM/MM) to elucidate and predict reaction mechanisms and reactivity that pure data-driven models struggle to capture.
• Experience building or using agentic AI / LLM-orchestrated workflows, tool use, multi-step reasoning, or…