Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design

Role Summary

Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design. Lead within the TuneLab platform, specializing in small molecule drug discovery with deep expertise in medicinal chemistry, ADME/Tox prediction, and generative modeling to accelerate lead optimization and candidate selection across the federated network.

Responsibilities

• Small Molecule Property Prediction: Architect and implement advanced multi-task learning models for small molecule properties including ADMET endpoints, solubility, permeability, metabolic stability, and off-target liabilities, handling diverse representations.
• Generative Chemistry Models: Design and deploy state-of-the-art generative models for de novo small molecule design, lead optimization, and scaffold hopping respecting synthetic accessibility and drug-likeness constraints.
• ADMET-Driven Design: Develop integrated prediction-generation pipelines optimizing across multiple ADMET properties while maintaining potency, using multi-objective optimization and Pareto front exploration.
• Chemical Space Navigation: Implement algorithms for efficient exploration of synthetically accessible chemical space, including reaction-aware generation and retrosynthetic planning integration.
• Structure-Activity Learning: Build models that learn structure-activity relationships from sparse, noisy bioactivity data across federated partners, including matched molecular pair analysis.
• Molecular Representation Learning: Develop self-supervised and semi-supervised methods to learn robust molecular representations for generalization to novel chemical series.
• Lead Optimization Workflows: Create AI-driven workflows for medicinal chemistry tasks including bioisosteric replacement, metabolic site prediction, and toxicophore removal while considering IP.
• Synthetic Feasibility Integration: Collaborate with synthetic chemists to ensure generated molecules are synthesizable, incorporating reaction prediction and building block availability.
• Cross-Partner Chemical Diversity: Leverage chemical diversity across federated partners while respecting boundaries, identifying complementary regions of chemical space for collaboration.
• Small Molecule Benchmarking: Establish benchmarks for property prediction and generation using public datasets and Lilly data.

Qualifications

• PhD in Computational Chemistry, Cheminformatics, Medicinal Chemistry, Chemical Engineering, or related field from an accredited college or university
• Minimum of 2 years of experience in small molecule drug discovery
• Strong experience with molecular property prediction and QSAR/QSPR methods
• Deep understanding of medicinal chemistry principles and ADMET optimization

Additional Preferences

• Experience with federated learning and distributed optimization in chemical applications
• Publications in top-tier venues on molecular generation or property prediction
• Expertise in graph neural networks and geometric deep learning for molecules
• Strong background in organic chemistry and synthetic feasibility assessment
• Experience with fragment-based and structure-based drug design
• Knowledge of PK/PD modeling and clinical translation
• Proven track record in developing generative models for molecular design
• Proficiency in cheminformatics tools (RDKit, DeepChem)
• Understanding of IP considerations in generative molecular design
• Experience with active learning and design-make-test-analyze cycles
• Portfolio mindset ensuring individual decisions align with TuneLab ecosystem goals

Education

• PhD in a relevant field (as listed in Basic Qualifications)

Additional Requirements

• This role is based at a Lilly site in Indianapolis, South San Francisco, or Boston with up to 10% travel (attendance expected at key industry conferences). Relocation is provided.