Lilly TuneLab is an AI-powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly's extensive proprietary pharmaceutical research data. Through federated learning, the platform enables Lilly to build models on broad, diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. This collaborative approach accelerates drug discovery by creating continuously improving AI models that benefit both Lilly and our biotech partners.
Requirements
- Strong experience with protein sequence analysis and structural biology
- Proven track record in machine learning applications to biological sequences
- Deep understanding of antibody structure-function relationships and immunology
- Experience with immune repertoire sequencing and analysis
- Expertise in protein language models and transformer architectures
- Proficiency in protein modeling tools (Rosetta, MOE, Schrodinger BioLuminate)
- Experience with federated learning in biological applications
Responsibilities
- Antibody Property Prediction: Build multi-task learning frameworks specifically for antibody properties including binding affinity, specificity, stability (thermal, pH, aggregation), immunogenicity, and developability metrics from sequence and structural features.
- Antibody Sequence Generation: Develop and implement generative models (transformers, diffusion models, evolutionary models) for antibody design, including CDR optimization, humanization, and affinity maturation while maintaining structural integrity.
- Structure-Aware Design: Integrate structural modeling and prediction (AlphaFold, ESMFold) with generative approaches to ensure generated antibodies maintain proper folding, CDR loop conformations, and epitope recognition.
- Developability Optimization: Create models that simultaneously optimize for multiple developability criteria including expression yield, solubility, viscosity, and post-translational modifications, crucial for manufacturing and formulation.
- Species Cross-Reactivity: Develop approaches to design antibodies with desired species cross-reactivity profiles for preclinical development, learning from cross-species binding data.
- Antibody-Antigen Modeling: Create models for predicting antibody-antigen interactions, epitope mapping, and paratope design, incorporating both sequence and structural information.
Other
- PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or related field from an accredited college or university
- Minimum of 2 years of experience in antibody or protein therapeutic development within the biopharmaceutical industry
- Publications on antibody design, protein engineering, or therapeutic development
- Knowledge of antibody manufacturing and CMC considerations
- Experience with display technologies (phage, yeast, mammalian)
