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At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Purpose

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.

The Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design plays an essential leadership role within the TuneLab platform, specializing in small molecule drug discovery. This position requires deep expertise in medicinal chemistry, ADME/Tox prediction, and small molecule optimization, combined with advanced data science capabilities in generative modeling and property prediction. The role will be instrumental in developing both predictive and generative models that accelerate small molecule lead optimization and candidate selection across the TuneLab federated network.

Key Responsibilities

+ **Small Molecule Property Prediction:** Architect and implement advanced multi-task learning models specifically for small molecule properties including ADMET endpoints, solubility, permeability, metabolic stability, and off-target liabilities, handling diverse chemical representations (SMILES, graphs, 3D conformations).

+ **Generative Chemistry Models:** Design and deploy state-of-the-art generative models (VAEs, diffusion models, flow matching, autoregressive models) for de novo small molecule design, lead optimization, and scaffold hopping that respect synthetic accessibility and drug-likeness constraints.

+ **ADMET-Driven Design:** Develop integrated prediction-generation pipelines that optimize molecules simultaneously across multiple ADMET properties while maintaining target potency, using techniques like multi-objective optimization and Pareto front exploration.

+ **Chemical Space Navigation:** Implement algorithms for efficient exploration of synthetically accessible chemical space, including reaction-aware generation, retrosynthetic planning integration, and fragment-based design approaches.

+ **Structure-Activity Learning:** Build models that learn and exploit structure-activity relationships from sparse, noisy bioactivity data across federated partners, including matched molecular pair analysis and activity cliff prediction.

+ **Molecular Representation Learning:** Develop self-supervised and semi-supervised methods to learn robust molecular representations from large collections of unlabeled compounds, enabling better generalization to novel chemical series.

+ **Lead Optimization Workflows:** Create AI-driven workflows for common medicinal chemistry tasks including bioisosteric replacement, metabolic site prediction, toxicophore removal, and property optimization while maintaining intellectual property considerations.

+ **Synthetic Feasibility Integration:** Collaborate with synthetic chemists to ensure generated molecules are practically synthesizable, incorporating reaction prediction models and building block availability into the generation process.

+ **Cross-Partner Chemical Diversity:** Design methods to leverage chemical diversity across federated partners while respecting competitive boundaries, identifying complementary regions of chemical space for collaborative exploration.

+ **Small Molecule Benchmarking:** Establish rigorous benchmarks for small molecule property prediction and generation using public datasets (ChEMBL, ZINC, PubChem) and proprietary Lilly data.

Basic 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

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.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is

$151,500 - $244,200

Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.

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