Post Doc Scientist, Structural Biology and Chemistry

About the role

At Gilead, our pursuit of a healthier world for all people has yielded a cure for hepatitis C, revolutionary improvements in HIV treatment and prevention as well as advancements in therapies for viral and inflammatory diseases and certain cancers. We set and achieve bold ambitions in our fight against the world’s most devastating diseases, united in our commitment to confronting the largest public health challenges of our day and improving the lives of patients for generations to come.

A highly motivated, attentive and self-driven postdoctoral researcher candidate will join the Computational Modeling Group as part of a three-year postdoctoral training program. The successful candidate will work at the forefront of innovation by utilizing cutting-edge AI/ML-based technologies. This is a three-year fixed-term postdoctoral position.

Responsibilities

• Detailed evaluation and analysis of AI tools and ML models which can be impactful for various stages of Small Molecule Drug Discovery, from hit identification through hit-to-lead to lead optimization.
• Clearly communicate findings via internal Gilead and external presentations, as well as through publishing in high-impact journals.
• Utilizing the rich set of historical Gilead data and structures, evaluate co-folding tools, potency models and ADME models in partnership with other Computational Chemists and AI/ML Scientists.
• Implement and analyze methods used to enable virtual screening of ultra-large libraries, including but not limited to Active Learning and Thompson Sampling.
• Investigate the feasibility of utilizing machine learning models to predict properties of heterobifunctional degraders (EPSA, permeability, etc.) using either SMILES strings or three-dimensional conformational ensembles as starting points.

Requirements

• Ph.D. and 0+ years of relevant research experience with deep expertise in AI/ML as applied to drug discovery and/or in Computational Chemistry.

Skills

• Knowledge of machine learning architectures relevant to chemistry and structural biology.
• Strong programming skills in Python and proficiency with ML frameworks.
• Ability to design, implement and evaluate robust model validation strategies, including uncertainty quantification and applicability domain assessment.
• Expertise with cheminformatics toolkits such as RDKit, OpenEye or Schrodinger desired.
• Excellent communication skills (both verbal and technical) and interpersonal skills.
• Must think critically and creatively, work independently, determine appropriate resources for resolution of problems, and have strong organizational and planning skills.

People Leader Accountabilities

• Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams.
• Develop Talent - understand the skills, experience, aspirations and potential of their employees and coach them on current performance and future potential. Ensure employees are receiving the feedback and insight needed to grow, develop and realize their purpose.
• Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives, and holding them to account. Provide the support needed to remove barriers and connect their team to the broader ecosystem.