Lead Scientist, Cheminformatics & AI in Drug Discovery

GSK; Stevenage, Cambridge; On-site; GBP 90,000 - 120,000; Full time

Job summary

GSK seeks a Principal Scientist in Cheminformatics to develop and embed advanced computational methods and predictive models to accelerate medicines discovery. You will work with large proprietary datasets and integrate outputs into decision-making workflows.

The role focuses on ML, cheminformatics and computational chemistry, with prototypes blending LLM-based reasoning with scientific tools and data to speed hypothesis generation and design cycles.

Qualifications

• Degree-level education in Cheminformatics, Computational Chemistry, Informatics or Life Sciences.
• Experience collecting and analysing complex biological/chemical data to build predictive models.
• Knowledge of machine learning, computational chemistry and cheminformatics methods.
• Programming and scripting in Python, Java, C/C++, or R.

Responsibilities

• Build, validate and deploy cheminformatics and ML models to inform go/no-go across drug-discovery programmes.
• Integrate with drug discovery teams to embed predictive models into decision-making.
• Prototype exploratory workflows combining LLM reasoning with cheminformatics tools and data.
• Contribute to production-quality code implementing state-of-the-art methods.
• Present validation results and capability developments to non-specialists.
• Collaborate in a multidisciplinary matrix team across geographies.

Skills

Artificial Intelligence

C

Education

PhD or MSc in Cheminformatics/Computational Chemistry/Informatic/Life Sciences

Tools

ChemAxon

RDKit

Pipeline Pilot

KNIME

Job description

GSK seeks a Principal Scientist in Cheminformatics to develop and embed advanced computational methods and predictive models to accelerate medicines discovery. You will work with large proprietary datasets and integrate outputs into decision-making workflows.

The role focuses on ML, cheminformatics and computational chemistry, with prototypes blending LLM-based reasoning with scientific tools and data to speed hypothesis generation and design cycles.