Sr. Director/Exec. Director of Cheminformatics for Early Molecule Discovery

Eli Lilly and Company-posted 7 days ago
Full-time • Executive
San Diego, IN
5,001-10,000 employees
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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. The Lilly Small Molecule Discovery group is an organization purpose-built to create molecules that make life better for people. We focus on using cutting edge science to unlock new approaches that can treat people suffering from diseases with poor treatment options. We continually challenge ourselves to deliver molecules that can provide breakthrough efficacy with the highest possible safety margins. We are dedicated to optimizing our mindset, technology, and processes for faster, more nimble execution. Our success is built on a culture that empowers innovative problem solving through open collaboration and individual accountability. Position Summary: Lilly’s newly created Early Molecule Discovery (EMD) team is responsible for small molecule hit - to - lead prosecution through the judicious application of best in class and novel approaches applied to pre-portfolio targets. We seek a cheminformatics group leader with a demonstrated ability of successfully applying in silico technologies to drive the discovery of quality lead - like molecules against hard to drug therapeutic targets. T his is a lead from the bench role, we therefore seek a hands-on candidate who can deftly apply cheminformatics and AI/ML technologies to impact library design, hit identification and (virtual) hit expansion efforts towards differentiated and efficacious lead molecules across multiple projects. The candi date will be highly collaborative and foster seamless collaborations with cross-functional partners to develop data-driven hypotheses and models that are leveraged by project teams to accelerate early molecule discovery efforts . The position requires excellent people skills, a positive can-do attitude and the ability to thrive in a highly interactive and fast-paced team environment.

  • “Lead from the bench” by judiciously building up and applying state of the art cheminformatics, ML/AI , and advanced analyses capabilities , to enable library design, hit identification, prioritization and hit - to - lead progression across multiple target classes and modalities .
  • Provide scientific leadership and strateg ic guid ance on cheminformatics and applied ML/AI approaches to drive data driven drug discovery.
  • E ncoura ge close collaborations and initiatives with computational colleagues, medicinal chemists and other cross function partners that culminate in the generat ion of well poised screening collections, quality models and testable hypotheses , enhanc ing our ability to deliver differentiated quality hit s and leads .
  • Ensure team success by providing guidance on the application of modern cheminformatics, ML/AI methods for: library design (e.g. diversity, focused, bridging, fragment, DEL collections ) ; analyzing large datasets (e.g. from HTS campaigns or omics data sets ) and building predictive (active learning) models from them; data mining internal and external data - sets/bases; enabling hit prioritization and expansion efforts; guid ing ligand-/fragment-based design activities .
  • Provide cheminformatics insight for new target identification and evaluation initiatives in the early space across a range of target s and binding mode types.
  • Proactively investigate new technologies that have the potential to accelerate EMD ’s ability to prosecute challenging targets and deliver quality differentiated leads . The candidate will also cultivate cross pillar collaborations with new technology , and Tech@Lilly colleagues to help guide and subsequently leverage transformative hit identification and hit-to-lead approaches.
  • Develop synthon-based search strategies to allow teams to leverage ever increasing virtual spaces without having to rely on brute force searches of fully enumerated spaces .
  • Ensure the timely delivery of quality data, rigorous analyses and robust models to project teams to accelerate hit identification and chemical series evaluation / evolution efforts.
  • To be adept at communicating results, and setting team a s well as larger organizational goals and expectations .
  • Engage with external teams upon the identification and elaboration of early lead molecules across multiple projects and mechanisms .
  • PhD in C heminformatics, C omputational Chemistry, or related field with 7 + years relevant research and/or industr ial exp erience .
  • Track record of successfully applying and developing cheminformatics workflows and tools that accelerate h it finding, hit expansio n, lead generation and library design efforts .
  • Expertise in data analytics , ML/AI modelling in the context of cheminformatics and a solid grasp of statistical principles .
  • Ability to create, sustain and model a culture of innovation, collaboration and dedication .
  • S trong scientific programming skills ( Python essential ) and experience building data visualizations and dashboards (e.g., in Spotfire) .
  • Demonstrated growth mindset , whilst maintaining close collaboration among computational chemistry leaders, elevating the global computational chemistry team as a whole .
  • Aptitude for building inclusive teams and commitment to mentoring earl y career computational chemists .
  • Demonstrated ability to identify and effectively champion new technologies culminating in successful drug discovery applications thereof .
  • Proactive in establishing and driving effective collaboration s with medicinal chemists and scientists form other disciplines to achieve project goals and timelines .
  • Ability to communicate effectively with team members, cross-functional colleagues and senior leadership .
  • Demonstrated ability to inspire and lead scientists to work across teams, functions and sites to achieve aspirational goals that accelerate portfolio deliveries.
  • Excellent understanding of the phases of dr ug discovery from t arget assessment through to candi date selection and the fundamental concepts of drug design , medicinal chemistry and ADME .
  • Good appreciation of computational chemistry and organic chemistry .
  • Familiarity with Large Language Models (LLMs) .
  • Experience using synthons and transformations to generate and interrogate virtual spaces.
  • Demonstrated experience in working collaboratively across various disciplines to meet project goals and timelines.
  • Agile and ready to change research priorities as necessary for success .
  • Ability to work independently and as an integral part of a larger collaborative team.
  • Highly organized with excellent analytical, documentation, time management, and multi-tasking skills.
  • Views personal success as a consequence of the team’s success.
  • Self-accountable for the timely delivery of progressable hits and eventual leads for uptake within the larger small molecule discovery organization .
  • Self-driven , hardworking , lab first, data dependent decision maker .
  • Excellent communication of goals and priorities across computational chemistry teams .
  • 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).
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