BioSpace
Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and
BioSpace, Indianapolis, Indiana, us, 46262
Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design
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Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design
role at
BioSpace
At Lilly, we unite caring with discovery to make life better for people around the world. Lilly is a global healthcare leader headquartered in Indianapolis, Indiana. Lilly’s 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 the community.
Purpose
Lilly TuneLab is an AI‑powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly’s 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.
Key Responsibilities
Architect and implement advanced multi‑task learning models specifically for small molecule properties including ADMET endpoints, solubility, permeability, metabolic stability, and off‑target liabilities using diverse chemical representations.
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.
Develop integrated prediction‑generation pipelines that optimize molecules simultaneously across multiple ADMET properties while maintaining target potency using multi‑objective optimization and Pareto front exploration.
Implement algorithms for efficient exploration of synthetically accessible chemical space, including reaction‑aware generation, retrosynthetic planning integration, and fragment‑based design approaches.
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.
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.
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.
Collaborate with synthetic chemists to ensure generated molecules are practically synthesizable, incorporating reaction prediction models and building block availability into the generation process.
Design methods to leverage chemical diversity across federated partners while respecting competitive boundaries and identifying complementary regions of chemical space for collaborative exploration.
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. If you require accommodation, please complete the accommodation request form for further assistance.
Lilly is a proud EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Actual compensation will depend upon a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $151,500 - $244,200. Full‑time employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
Lilly offers a comprehensive benefit program to eligible employees, including company‑sponsored 401(k); pension; vacation; medical, dental, vision, prescription drug benefits; flexible benefits; life insurance; death benefits; certain time off; leave of absence benefits; and well‑being benefits.
#WeAreLilly
#J-18808-Ljbffr
Machine Learning Scientist/Sr Scientist - Small Molecule Property Prediction and Generative Design
role at
BioSpace
At Lilly, we unite caring with discovery to make life better for people around the world. Lilly is a global healthcare leader headquartered in Indianapolis, Indiana. Lilly’s 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 the community.
Purpose
Lilly TuneLab is an AI‑powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly’s 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.
Key Responsibilities
Architect and implement advanced multi‑task learning models specifically for small molecule properties including ADMET endpoints, solubility, permeability, metabolic stability, and off‑target liabilities using diverse chemical representations.
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.
Develop integrated prediction‑generation pipelines that optimize molecules simultaneously across multiple ADMET properties while maintaining target potency using multi‑objective optimization and Pareto front exploration.
Implement algorithms for efficient exploration of synthetically accessible chemical space, including reaction‑aware generation, retrosynthetic planning integration, and fragment‑based design approaches.
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.
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.
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.
Collaborate with synthetic chemists to ensure generated molecules are practically synthesizable, incorporating reaction prediction models and building block availability into the generation process.
Design methods to leverage chemical diversity across federated partners while respecting competitive boundaries and identifying complementary regions of chemical space for collaborative exploration.
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. If you require accommodation, please complete the accommodation request form for further assistance.
Lilly is a proud EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Actual compensation will depend upon a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $151,500 - $244,200. Full‑time employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
Lilly offers a comprehensive benefit program to eligible employees, including company‑sponsored 401(k); pension; vacation; medical, dental, vision, prescription drug benefits; flexible benefits; life insurance; death benefits; certain time off; leave of absence benefits; and well‑being benefits.
#WeAreLilly
#J-18808-Ljbffr