Senior Scientist - CADD

Ryvu Therapeutics

Kraków, Poland

As part of the Data Science team, your role will involve partnering with research scientists from the Medicinal Chemistry and Biology departments on drug discovery initiatives including tasks such as target identification, druggability assessment, hit discovery and development and lead optimization. You will employ a diverse set of CADD, cheminformatics and ML techniques to deliver timely project insights advancing pre-clinical phase drug discovery projects.

KEY RESPONSIBILITIES

Take part in hit identification activities by running vHTS campaigns including ligand-based, target-based and ML approaches. Efficient screening of ultra-large compound libraries. Close collaboration with Compound Screening department during HTS campaigns;
Apply molecular modelling tools to refine or predict target structure and small molecule binding mode (homology modelling, docking, MD simulations);
Support DMTA cycles in drug discovery projects by applying computational tools to design and score virtual compounds (free energy perturbation, active learning models, ADME prediction models);
Perform target druggability assessment to assist in selection of novel targets;
Work within multidisciplinary drug discovery teams and contribute to project planning, deliverables and timelines;
Apply existing machine learning models to projects, and collaborate with AI researchers to refine algorithms as needed;
Drive scientific method development in specific areas of cheminformatics/AI, such as virtual screening, model building for on-target potency or property prediction, molecule generation, binding free energy calculation or target druggability assessment;
Create, enhance, and deploy streamlined computational workflows that can be shared within CADD/ML and chemistry teams for improved efficiency.

YOUR BACKGROUND

Ph. D in Computational Chemistry, Cheminformatics or a related discipline;
4+ years of industry experience, or 2+ years in industry plus 2+ years academic post-doc experience;
Experience in structure-based drug discovery, virtual screening of ultra-large compound libraries, MD simulations, free energy perturbation, chemical space analysis and clustering, druggability assessment;
Strong communication skills; ability to formulate scientific hypotheses and deliver conclusions of computational experiments in a clear and comprehensible way within multi-disciplinary teams;
Thorough understanding of drug discovery principles, such as medicinal chemistry fundamentals, structural biology, ADME and assay technology;
Working knowledge of Python, git, docker;
Familiarity with ML/AI approaches in drug discovery;
Fluency in written and spoken English is mandatory.

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