Associate Principal Scientist - Cheminformatics
Core
Developing next-generation small molecule and PROTAC medicines using state-of-the-art in silico methods for molecular modelling, ligand design, property predictions, and data analysis.
Role type
Associate Principal Scientist (Cheminformatics)
Builds
Data pipelines, predictive models, library designs, and SAR-analysis to improve potency, selectivity, activity, and ADMET properties of drug compounds.
Domain
BioPharma / Drug Discovery / Cheminformatics
Deliverable
production ML models | product features
Required skills
Machine learning and deep learning (generative design, QSA/PR, reaction prediction), ligand-based design, scientific computing, Python, cheminformatics toolkits (RDKit, OpenEye), data standards (SMILES/SMARTS, InChI, SDF), ML frameworks (scikit-learn, PyTorch, TensorFlow), cloud basics
Preferred skills
Structure-based design, molecular dynamics, modern FEP methods
Technologies
RDKit, OpenEye, scikit-learn, PyTorch, TensorFlow, Python, Perl, C, C++, Java, R
Responsibilities
Apply computational chemistry and AI approaches to deliver data pipelines and predictive models; identify scientific improvement areas in computational sciences; collaborate with external partners and colleagues across AZ sites; maintain awareness of literature and developments in the field.
Seniority
Associate Principal, hands-on IC