PE Global are currently recruiting for a Computational Chemistry Expert Researcher for a contract role with a leading multinational Pharma client based in Arlington Square.
We are seeking a cheminformatics scientist based in UK to join our Genetic Medicine team with demonstrated ability to successfully apply traditional and state-of-the-art cheminformatics methods and AI/ML technologies to drive the chemical space experimental exploration of lipids forming tLNP (Targeted Lipids Nanoparticles), by leading generation and iterative refinement of lipids libraries capturing structural features, chemical intuition and molecular modeling insights.
Job Responsibilities
- Identify or develop optimal molecular representations for lipids involved in lipid nanoparticles.
- Develop workflows to analyze lipid structures in internal and public datasets, classify them, and extract key physicochemical features.
- Identify optimal internal and external building blocks compatible with available chemistry to engineer lipid structures, including identification of motifs that can lead to specific formulation and in-vivo readouts.
- Generate and refine a virtual lipid library that is iterated by data-structure analysis (cheminformatics and AI/ML methods), chemical intuition, and molecular modeling insights. This generation might leverage reaction-based enumerations by considering available chemistry and building blocks.
- Create, validate and assess performance of predictive models relating lipid’s structure-based descriptors with formulation and in-vivo readouts. Specifically, create predictive models of Molecule-Particle relationships.
- Generate workflows for diversity selection and prioritization of lipids to be synthesized to maximize chemical space exploration and model prediction applicability domain, considering throughput and synthesis limitations (i.e. Yield, purification, etc.).
- Ensure consistent analysis and model predictions by lipid topology (i.e. Whole lipid, head, linker, tail(s)), extracting information about the possible role of each part of the lipid on the formulation and in-vivo readouts.
- Maintain high standards in data quality and curation workflows, by understanding all dimensions of the lipid’s and the nanoparticle’s data (computed properties, measured properties, synthesis/purification of lipid, formulation of particles, etc.).
- Influence the experimental setup for synthesis and formulation, to ensure high quality curated data production to inform the predicted models (i.e. Consistent formulation conditions, etc.).
- The candidate will also cultivate cross functional cheminformatics and computational chemistry collaborations
Requirements
- PhD in Cheminformatics, Computational Chemistry, or related field with 4+ years of experience in relevant research and/or industrial experience.
- Proven experience applying cheminformatics and AI/ML methods to lipid analysis and design.
- Proven experience in data analytics, AI/ML modelling in the context of cheminformatics and solid grasp of statistical principles.
- Strong scientific programming skills (Python essential) and experience building data visualizations.
Additional Skills/Preferences
- Understanding of tLNP formulation process and particle measured properties.
- Understanding of tLNP components roles, including key properties of each component and of the tLNP as a whole (i.e. PKa vs apparent pKa, etc.).
- Understanding of synthesis and purification challenges of lipids.
- Deep knowledge of chemoinformatics toolkits, and ability to adapt to/learn new tools and methods.
- Deep knowledge of topological descriptors describing lipids structures.
- Experience in the interface of AI-based agents and chemoinformatics is valuable.
- Willingness to explore, among computational scientists, physics-based methods applied to lipids and tLNP.
Interested candidates should submit an updated CV.
***Please note our client cannot assist with any visa sponsorship and candidates must have the correct visa to live and work in UK***