ADMET Data Pipeline Development: Design, build and maintain scalable pipelines for ingesting, processing, and harmonizing diverse ADMET datasets from public sources (e.g., ChEMBL, PubChem) and proprietary assays., Data Harmonization: Standardize heterogeneous ADMET data formats (e.g., in vitro assays, in silico predictions) across network participants to enable modelling readiness of the data, Model-Ready Dataset Curation: Preprocess raw ADMET data (e.g., normalizing units, handling missing values) to support AI/ML model training for a variety of endpoints (like bioavailability, hERG inhibition, or CYP450 interactions), Data Quality Assurance: Implement and automate validation checks to ensure ADMET data integrity, Cross-Functional Integration: Work with computational chemists to optimize data structures for AI-driven ADMET models (e.g., graph-based representations for metabolic pathways), Work with our customers and potentially academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving ADMET data, including leveraging and harmonizing assay data from different sources., Collaborate cross-functionally to ensure data and resulting models address real-world drug discovery needs., Mentor and guide team members on a content level, supporting the planning and breakdown of complex ADMET data preparation., Influence strategic decisions on data infrastructure and data quality assurance, Contribute to publications or open-source contributions where relevant.
