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P

Cheminformatic Researcher

PE Global International Bracknell
32 - 40 hour
new


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    P

    Cheminformatic Researcher

    PE Global International Bracknell
    32 - 40 hour
    new
    Status Open
    Apply now

    Apply on the employer's website


    What we ask

    Education

    No minimum education required

    What we offer

    Hours
    32 to 40 hours per week
    Employment type
    contract

    Job description

    PE Global are currently recruiting for a Cheminformatics researcher for a contract role with a leading multinational Pharma client.
    The role will be based in Arlington in Bracknell, in the office a minimum of 2 days per week.

    The pay rate is between £36.84 and £49.12 per hour PAYE

    Looking for candidates with experience applying 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 contributing to the generation and iterative refinement of lipids libraries capturing structural features, chemical intuition and molecular modeling insights.

    Job Responsibilities
    • Help on the identification or development of 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.
    • Process 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.
    • Contribute to the generation and refinement of 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.
    • Contribute with 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.).
    • Help influencing 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 concepts (formulation process, particle measured properties, components roles, including key properties of each component and of the tLNP as a whole - i.e. pKa vs apparent pKa, etc.) is valued.
    • Deep knowledge of chemoinformatics toolkits, and ability to adapt to/learn new tools and methods.
    • Understanding of synthesis and purification challenges of lipids or small molecules is preferred.
    • Knowledge of topological descriptors of small molecules or lipids is valued.
    • 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***

    Although it is not possible for us to respond to all applications, we at PE Global will do our upmost to give you feedback on your application. You have sent your Cv into us as a company and even though you have sent your CV to a particular position, we are making the reasonable assumption that you are active on the job market and as part of our normal recruitment service we will discuss other suitable positions with you. You are free to opt out of this so please specify in your application to us if you just want to be contacted in relation to a specific vacancy. Your Cv is sent to a central recruitment inbox which a number of people in the applicable PE Global division have access to and so this means that you might not be contacted by the named person in this advert
    Apply now

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    Apply now

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