Teaser Text

The Faculty of Science and Leiden Academic Centre for Drug Research (LACDR) are looking for candidates for a PhD Position (Doctoral Candidate 10) in the Marie Skłodowska-Curie Doctoral Network ANA-KIN

ANA-KIN is a Marie Skłodowska-Curie Actions (MSCA) Doctoral Network funded by the European Union’s Horizon Europe programme. Over the past two decades, small-molecule kinase inhibitors have transformed cancer therapy, yet nearly all of them target the highly conserved ATP-binding site, which limits selectivity and allows resistance to emerge. ANA-KIN aims to train a new generation of interdisciplinary researchers capable of redefining kinase drug discovery beyond the ATP paradigm, by developing the experimental and computational tools, modular assays and workflows needed to systematically target non-conserved regulatory interfaces, such as kinase dimerization and substrate-binding sites. Proof-of-concept will be demonstrated on three representative kinases: EGFR, BRAF and the understudied “dark” kinase LTK.

ANA-KIN brings together 11 beneficiaries and 9 associated partners across Europe and the USA, including Aristotle University of Thessaloniki (coordinator), University of Crete, Goethe University Frankfurt, Saarland University, EPFL, CNRS, Pompeu Fabra University, VIB, KU Leuven, INSERM and Leiden University, together with industrial partners such as AstraZeneca, Novartis, Servier and Skylight Therapeutics. The network combines chemical biology, structural biophysics (single-molecule FRET, cryo-EM, nanodiscs), AI-driven drug design and cell biology in an integrated learn–design–test loop. Twelve Doctoral Candidates (DCs) will each pursue an individual research project, benefit from co-supervision across institutions and sectors, carry out two research secondments at partner organizations, and follow a structured network-wide training program covering scientific, transferable, open-science and entrepreneurial skills.

In this PhD project (DC10, Work Package 3 – Discovery), you will develop deep-learning architectures that integrate text-mined, cheminformatic, and biophysical descriptors to predict interface binding, allosteric signatures and resistance liabilities of kinase ligands. The models will learn jointly across peptide and small-molecule modalities and will be used to generate hybrid peptidomimetic design hypotheses. You will translate these models into consortium-wide prioritisation dashboards that support hit triage and analogue selection in the discovery campaigns of ANA-KIN, and work in close reciprocal feedback with DC9 (Saarland University) on structure-based and machine-learning pipelines. Model predictions will be validated prospectively with the experimental partners in the network.

The expected results are a resistance-aware AI workflow for non-ATP interface ligands, accompanied by public models, datasets and interpretable feature maps, and prospective design case studies demonstrating improved optimisation trajectories. To reinforce structural grounding and experimental validation, you will carry out two secondments: at VIB (Belgium, Prof. Ruslan Efremov; 4 months) to extract structural classes and conformational features from cryo-EM data and use them to refine model constraints, and at Skylight Therapeutics (Belgium, Dr. Joshua Salafsky; 3 months) to test model predictions using second-harmonic generation (SHG)-based conformational profiling. The project will be supervised by Prof. Gerard J.P. van Westen (Leiden University), with Prof. Ruslan Efremov and Dr. Joshua Salafsky as co-supervisors.

What you will do

• Conduct original and novel research in deep learning, AI, and cheminformatics for kinase drug discovery;
• Develop, benchmark and openly share models, datasets and decision-support dashboards following FAIR and open-science principles;
• Collaborate closely with experimental and computational DCs in the network, and carry out two research secondments abroad (VIB and Skylight Therapeutics, Belgium);
• Publish and present scientific articles at international journals and conferences;
• Participate in the ANA-KIN network-wide training events, schools and outreach activities;
• Contribute to educational activities;
• Write a dissertation.

What you bring

• A MSc degree in Computer Science, AI, (Computational) Chemistry, Bioinformatics, Biopharmaceutical Sciences, or a relevant related field;
• Good knowledge of and experience with machine learning and deep-learning techniques (e.g. PyTorch);
• Good knowledge of and experience with cheminformatics and/or computational chemistry (e.g. RDKit, molecular descriptors, docking);
• Interest in structural biology, protein kinases and drug discovery; affinity with peptide or biophysical data is an advantage;
• Highly motivated to both perform foundational research and apply the developed methods to real-world problems; creative, ‘making things work’ mentality, independent, and communicative team player;
• Willingness to travel and to carry out research secondments at partner institutions abroad;
• Excellent programming skills (preferably Python);
• Good academic writing skills and interest in participating in educational activities;
• Excellent proficiency in English (oral and written).

MSCA eligibility criteria

• At the date of recruitment, candidates must not have been awarded a doctoral degree;
• Mobility rule: candidates must not have resided or carried out their main activity (work, studies, etc.) in the Netherlands for more than 12 months in the 36 months immediately before the date of recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status are not taken into account.

Where you will work

What we offer

What we value

Promoting an inclusive community is central to Leiden University’s values and vision. We strive to be an open community in which all students and staff members feel valued and respected, and are able to fully develop themselves. We consider an inclusive academic environment, where students and staff can share their experiences and viewpoints, to be essential for the development of talent.

Want to apply or find out more?

• Acquisition in response to this vacancy is not appreciated. If you nevertheless choose to send us CVs, no rights can be derived from this. #LI-Hybrid