Postdoctoral Research Associate in Computational Chemistry

About the role:

Candida albicans is a major fungal pathogen that can cause severe morbidity and life-threatening infection. A key virulence factor is candidalysin, a peptide toxin secreted by C. albicans hyphae that drives epithelial damage, cell activation and mucosal inflammation.

The postdoctoral researcher will lead the computational chemistry component of a multidisciplinary programme to identify and optimise peptide, peptidomimetic and small-molecule inhibitors of candidalysin. The role will combine molecular modelling, molecular docking, molecular dynamics simulations and structure-based design to understand candidalysin structure, conformational behaviour and interactions with candidate inhibitors.

The researcher will design and optimise peptide and peptidomimetic inhibitors, identify key residues and structural features governing binding and activity, and undertake virtual screening of small-molecule libraries to identify new chemical matter with potential to inhibit candidalysin function. Where appropriate, they will investigate candidalysin in membrane-associated environments and examine how inhibitors may alter toxin conformation, oligomerisation, membrane interaction or other processes required for activity.

A major focus of the role will be the integration of computational predictions with biophysical and biological data. The researcher will rationalise structure–activity relationships, explain differences in inhibitor potency and activity, and use emerging experimental results to refine computational models and guide subsequent design cycles. They will also apply appropriate computational methods to assess binding interactions, conformational behaviour, physicochemical properties and compound developability.

The researcher will work closely with the synthetic medicinal chemist and with Professor Julian Naglik’s team to prioritise compounds for synthesis and biological evaluation, helping to drive an efficient design–make–test cycle.

The researcher will be funded by the BBSRC and based in the laboratory of Professor Miraz Rahman at King’s College London, where they will undertake independent, original research within a multidisciplinary drug-discovery programme.

This is a full time post (35 Hours per week), and you will be offered a fixed-term contract until 31/12/2027

About you:

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential Criteria:

1. PhD in Computational Chemistry, Medicinal Chemistry, Chemical Biology or a closely related discipline (candidates who have recently submitted their PhD thesis are eligible to apply).
2. Research experience in computational medicinal chemistry, including structure-based design & optimisation of bioactive molecules.
3. Demonstrable expertise in molecular modelling, molecular docking and molecular dynamics simulations, including analysis of protein/peptide–ligand interactions.
4. Experience in the computational design and evaluation of peptides, peptidomimetics and/or small-molecule inhibitors, including interpretation of structure–activity relationships.
5. Proficiency with relevant computational chemistry software packages, such as Schrödinger Suite, AutoDock Vina, BIOVIA Discovery Studio or equivalent platforms.
6. Ability to integrate computational predictions with biophysical, biochemical and biological data to rationalise inhibitor activity and guide iterative compound design.
7. Track record of high-quality research, including publications in peer-reviewed journals of international standing.
8. Excellent organisational, analytical, communication & teamwork skills, with the ability to work effectively within a multidisciplinary research team.

Desirable Criteria:

1. Experience in advanced molecular dynamics approaches, membrane-associated simulations, binding-energy calculations and/or other computational methods relevant to peptide–protein interactions.
2. Record of presenting scientific research at national and/or international conferences.
3. Experience in computational workflow development, scripting, cheminformatics and/or bioinformatics approaches relevant to drug discovery.
4. Experience of multidisciplinary collaboration with synthetic chemists, biophysicists and/or biological scientists, particularly in structure-guided inhibitor discovery.
5. Experience of supervising, mentoring or supporting MSc, PhD or other research students.

Interviews are due to be held in November/December.