Cheminformatics Researcher

Join the forefront of biotechnology, where your expertise in cheminformatics can help unlock breakthrough therapies through data-driven lipid discovery.

R&D Partners is seeking a highly skilled Cheminformatics Researcher to join a Genetic Medicine team in the UK. This role focuses on applying advanced cheminformatics methods and AI/ML technologies to explore chemical space and refine lipid libraries for targeted lipid nanoparticles (tLNP). The position offers an exciting opportunity to contribute to cutting-edge research in biotechnology.

Please note that to be considered for this role you must have the right to work in this location.

• Develop optimal molecular representations for lipids involved in lipid nanoparticles.
• Create workflows to analyze lipid structures from internal and public datasets, classify them, and extract key physicochemical features.
• Process building blocks compatible with available chemistry to engineer lipid structures, identifying motifs for specific formulation and in-vivo readouts.
• Contribute to the generation and iterative refinement of virtual lipid libraries using cheminformatics, AI/ML methods, chemical intuition, and molecular modeling insights.
• Build predictive models linking lipid structure-based descriptors to formulation and in-vivo readouts, including Molecule-Particle relationships.
• Design workflows for diversity selection and prioritization of lipids for synthesis, maximizing chemical space exploration while considering synthesis limitations.
• Ensure consistent analysis and model predictions by lipid topology, extracting insights into the roles of lipid components in formulation and in-vivo readouts.
• Maintain high standards in data quality and curation workflows, understanding all dimensions of lipid and nanoparticle data.
• Influence experimental setups for synthesis and formulation to ensure high-quality curated data production.
• Foster cross-functional collaborations in cheminformatics and computational chemistry.

• PhD in Cheminformatics, Computational Chemistry, or related field.
• Proven experience in cheminformatics methods for chemical library design (e.g., combinatorial design, SAR/SPR analysis, feature selection, molecular representation).
• Expertise in data analytics and AI/ML modeling within cheminformatics, with a preference for experience in small molecules or lipids.
• Strong scientific programming skills, particularly in Python, and proficiency in building data visualizations.

• Understanding of tLNP concepts, formulation processes, and particle properties.
• Deep knowledge of chemo informatics toolkits and adaptability to new tools/methods.
• Familiarity with synthesis and purification challenges of lipids or small molecules.
• Knowledge of topological descriptors for small molecules or lipids.
• Experience at the interface of AI-based agents and cheminformatics.
• Willingness to explore physics-based methods applied to lipids and tLNP.

For more information, please contact Grant Van Schalkwyk.