Advisor - Fragment-based Computational Design
Advisor - Fragment-based Computational Design

Advisor - Fragment-based Computational Design

London Full-Time 40000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Join our team to revolutionise drug discovery using cutting-edge computational design.
  • Company: Lilly is a global healthcare leader dedicated to improving lives through innovative medicines.
  • Benefits: Enjoy a hybrid work environment, competitive salary, and opportunities for professional growth.
  • Why this job: Be part of a mission-driven team that values innovation and collaboration in healthcare.
  • Qualifications: Ph.D. or M.S. in relevant fields with hands-on experience in fragment-based lead discovery.
  • Other info: Work in a dynamic lab setting with a focus on diversity and inclusion.

The predicted salary is between 40000 - 60000 £ per year.

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.

About Lilly: Lilly is a global healthcare leader that unites caring with discovery to make life better for people around the world. We were founded more than a century ago by a man committed to creating high-quality medicines that meet real needs, and today we remain true to that mission in all our work. Across the globe, Lilly employees work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to communities through philanthropy and volunteerism.

Lilly Small Molecule Discovery is an organisation 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 optimising 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.

Discovery Technology and Platforms is a newly established function within this organisation. Its mission is to accelerate molecule discovery by building highly optimised foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and intentionally investing in novel technologies and capabilities.

About the Novel Platforms Team: The Novel Platforms team identifies and develops transformative technologies to improve hit finding, mechanism of action exploration, and molecular design. Through agile, hypothesis-driven research, we turn high-potential ideas into mature, robust platforms embedded across Lilly’s discovery organisation.

Position Summary: Lilly Small Molecule Discovery is seeking a Computational Scientist to join our Novel Platforms Fragment-based Discovery team. We are reimagining Fragment-Based Lead Discovery (FBLD) by embedding computation at its core—from data curation and modeling to design, triage, and decision-making. You will be a key contributor to an interdisciplinary initiative spanning structural biology, biophysics, chemistry, and AI systems, with the mission of transforming how fragments are discovered, evolved, and optimised. You’ll develop and integrate cutting-edge tools, to extract insight from diverse experiments—SPR, NMR, X-ray crystallography, cryo-EM, virtual screens—and convert them into actionable hypotheses. Your work will directly impact how Lilly identifies, and advances leads against challenging targets.

Key Responsibilities

  • Utilise state-of-the-art software to grow, link and merge fragment hits into novel chemical designs as well as work with informaticians to develop novel methods to improve this process.
  • Triage and extract key pharmacophoric and structural insights from early biophysics and fragment screening data.
  • Build and improve on internal proprietary fragment libraries and develop bespoke cassette relevant to the target class being explored.
  • Coordinate with internal and external chemistry resources to prioritise and ensure timely delivery of the chemical matter.
  • Collaborate closely with biophysicists, structural biologists, medicinal chemists, and AI discovery teams.

Innovation & External Awareness

  • Stay at the forefront of computational FBLD innovations, AI-enhanced design, and in silico fragment evolution.
  • Evaluate and integrate best-in-class tools, open-source frameworks, and academic advancements.
  • Drive tool adoption and best practices in fragment modeling and AI-integrated design.

Required Qualifications

  • Ph.D. with 3+ years or M.S. with 5+ years of industry experience in structural biology, chemical biology, or related fields.
  • Hands-on experience with software pertaining to FBLD such as Maestro/Schrodinger, ICM-Pro, GINGER, FrankenROCS, Fragmentstein, cluster4x, PanDDA, and the use of scripting to automate their use, ideally in a High-Performance Computing (HPC) environment.
  • Proven track record of FBLD.
  • Proven ability to independently design experiments, troubleshoot challenges, and effectively collaborate in a fast-paced environment.

Additional Preferences:

  • Hands-on experience with biophysical assays for protein characterisation, binding analysis and Molecular Dynamics (MD).
  • Experience in X-ray crystallography and cryo-EM data collection, processing, protein model and ligand building.
  • Experience in developing methods to identify ligand-ready cryptic pockets.
  • Passion for platform development, continuous optimisation, and operational excellence with a strong emphasis on data quality and reproducibility.
  • Adaptability and resilience in a fast-paced, evolving research environment.

Additional Information

Work Environment: This position’s work environment is in an office and a laboratory. Physical Demands/Travel: Travel up to 10% of the time.

EMBRACING DIVERSITY: Embracing diversity is at the core of our long-held value of respect for people. It is the lens through which we understand and respond to the unique needs of the millions of individuals who depend on our medicines. For us, embracing diversity means understanding, respecting, and valuing differences, including but not limited to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other legally protected status. The greatest measure of our diversity efforts is our ability to attract and retain exceptional employees who feel comfortable in a culture that supports them being themselves. 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 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 does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

Advisor - Fragment-based Computational Design employer: Eli Lilly and Company

Eli Lilly and Company is an exceptional employer that prioritises employee well-being and professional growth, offering a dynamic work culture in Bracknell, UK. With a commitment to innovation in healthcare, employees are empowered to collaborate across disciplines, engage in cutting-edge research, and contribute to life-changing discoveries. The hybrid work model and focus on diversity ensure a supportive environment where every individual can thrive and make a meaningful impact.
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Contact Detail:

Eli Lilly and Company Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Advisor - Fragment-based Computational Design

✨Tip Number 1

Familiarise yourself with the specific software mentioned in the job description, such as Maestro/Schrodinger and ICM-Pro. Having hands-on experience or even a basic understanding of these tools can set you apart during interviews.

✨Tip Number 2

Network with professionals in the field of fragment-based lead discovery. Attend relevant conferences or webinars to connect with industry experts and gain insights into current trends and challenges in computational design.

✨Tip Number 3

Prepare to discuss your previous projects that align with the responsibilities outlined in the job description. Be ready to explain how your work has contributed to advancements in molecular design or computational methods.

✨Tip Number 4

Stay updated on the latest research and innovations in computational FBLD and AI-enhanced design. Being knowledgeable about recent developments will demonstrate your passion for the field and your commitment to continuous learning.

We think you need these skills to ace Advisor - Fragment-based Computational Design

Ph.D. in structural biology, chemical biology, or related fields
Hands-on experience with FBLD software (e.g., Maestro/Schrodinger, ICM-Pro, GINGER)
Scripting skills for automation in High-Performance Computing (HPC) environments
Proven track record in Fragment-Based Lead Discovery (FBLD)
Ability to design experiments and troubleshoot challenges independently
Collaboration skills with biophysicists, structural biologists, medicinal chemists, and AI teams
Experience with biophysical assays for protein characterisation and binding analysis
Knowledge of Molecular Dynamics (MD) simulations
Experience in X-ray crystallography and cryo-EM data collection and processing
Development of methods for identifying ligand-ready cryptic pockets
Passion for platform development and operational excellence
Strong emphasis on data quality and reproducibility
Adaptability and resilience in a fast-paced research environment

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights relevant experience in structural biology, chemical biology, and computational design. Use specific examples that demonstrate your hands-on experience with FBLD software and your ability to collaborate in interdisciplinary teams.

Craft a Compelling Cover Letter: In your cover letter, express your passion for the role and how your background aligns with Lilly's mission. Mention specific projects or experiences that showcase your skills in computational design and your commitment to improving healthcare outcomes.

Highlight Technical Skills: Clearly list your technical skills related to the job, such as experience with Maestro/Schrodinger, ICM-Pro, and other relevant software. Provide context on how you've used these tools in past roles to solve complex problems.

Showcase Innovation and Adaptability: Demonstrate your ability to stay at the forefront of computational innovations. Include examples of how you've adapted to new technologies or methodologies in your previous work, particularly in fast-paced environments.

How to prepare for a job interview at Eli Lilly and Company

✨Showcase Your Technical Expertise

Make sure to highlight your hands-on experience with relevant software like Maestro/Schrodinger and ICM-Pro. Be prepared to discuss specific projects where you've successfully applied these tools in a Fragment-Based Lead Discovery context.

✨Demonstrate Collaborative Skills

Since the role involves working closely with biophysicists, structural biologists, and AI teams, be ready to share examples of how you've effectively collaborated in interdisciplinary settings. This will show that you can thrive in a team-oriented environment.

✨Stay Updated on Innovations

Familiarise yourself with the latest advancements in computational FBLD and AI-enhanced design. Discussing recent innovations or tools you've evaluated can demonstrate your commitment to staying at the forefront of the field.

✨Prepare for Problem-Solving Scenarios

Expect questions that assess your ability to troubleshoot challenges in a fast-paced research environment. Prepare to discuss specific instances where you've designed experiments or overcome obstacles, showcasing your innovative problem-solving skills.

Advisor - Fragment-based Computational Design
Eli Lilly and Company
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