Sr. Scientist, Computational Chemistry

About the Role:

Responsible for driving the execution of computational driven methodologies to help design optimized compounds with balanced properties (targets, DMPK, in-vivo) in drug discovery programs. Provides impactful insights and collaboration on projects ranging from early lead identification to the late-stage optimization of advanced projects. Serves as a subject matter expert in 1 or more molecular discovery approaches such as:
Structure-based Design & FEP, Virtual Screening, Quantum Chemistry, Machine Learning / Modern AI etc. Responsible for the communication and presentation of computationally derived results to the discovery project teams to facilitate effective decision-making & demonstrate an independent work style while being fully collaborative & team-oriented.

• This is an on-site role requirement (in San Diego site)
• Prior experience with independently driving drug discovery projects is highly desired for this role
• Domain knowledge of most or all the following:
  Physical Chemistry, Computational Chemistry, Cheminformatics, Protein Modeling/ Molecular Dynamics, Molecular Modeling as employed for the optimization of lead compounds
• Molecular Modeling applied to compound design and optimization such as Pharmacophore Analyses, Library Design, virtual HTS, Diversity/Similarity Analyses, Scaffold Hopping
• Protein-Ligand Modeling that includes well-known commercial docking tools as well as Molecular Dynamics methods, & experience with post-docking processing
• Develops advanced Machine Learning/AI in-silico models for modeling DMPK/in-vitro Biology endpoints, for front-loading projects with appropriate predictive information, & enable more efficient MPO analyses & new compound designs
• May have an exposure to harnessing large datasets including public domain datasets of chemistry related to various targets and/or chemogenomic nature
• Ability to demonstrate an overall application of several integrated approaches (ex: ML derived predictions, Modeling SBD/ LBD) to progress compound design contextual in drug discovery, exhibiting innovative approaches that tweak commercial solutions
• Independently driving forward Drug Discovery projects involving Structure Based Design including, but not limited to, target protein flexibility considerations
• Serves as an independent Comp Chem representative on Project teams, and works with minimal additional guidance, while demonstrating clear impact on project's chemical series evolution
• Advances the company's computational platform with expert knowledge providing innovative ideas to make significant contributions, that is aligned with team's strategy to progress compounds forward for multiple projects
• Leads 1-2 advanced technology platforms, defining new computational methods, in tandem with self-interest and relevance to projects, to help augment Neurocrine's Computational Chemistry platform for Drug Discovery
• Engages stakeholders from multiple Research functions to deliver and/or exchange key results
• Drives and/or aligns with strategies emanating from project teams, department and computational chemistry group
• Provides training and/or supervision to junior staff, as needed
• Other duties as assigned

• BS/BA degree in Chemistry and 5+ years of relevant experience, including familiarity utilizing any or all of the following:
  Protein-Ligand modeling, Molecular Dynamics, Homology Modeling is preferred OR
• MS/MA degree in Chemistry and 3+ years of similar experience noted above OR
• PhD in Computational Chemistry or related field and some relevant experience. Postdoctoral experience in Cheminformatics preferred
• Experience in one or more of the following Molecular Modeling domains is highly desirable:
  Protein Ligand docking & post-docking processing, Molecular Dynamics, Homology Modeling, Quantum Chemistry, Pharmacophore Analyses and Diversity Analyses
• Comfortable with routine programming & scripting including python, C++ and/or R
• Working knowledge about computational technologies for the assessment of early-stage targets (ex: druggability)
• Familiarity with well-known commercial molecular modeling software suites is also desirable such as Schrodinger, CCG or Open Eye
• Demonstrates solid level of understanding project / group goals and methods
• Consistently recognizes anomalous and inconsistent results and interprets experimental outcomes
• Able to explain the process behind the data and implications of the results
• Strong knowledge of one or more scientific disciplines, becoming expert in one discipline
• Strong knowledge of scientific principles, methods and techniques
• Strong knowledge and demonstrated ability working with a variety of laboratory equipment/tools
• Ability to work as part of a team; may train lower levels
• Excellent computer skills
• Strong communications, problem-solving, analytical thinking skills
• Detail oriented yet can see broader picture of scientific impact on team
• Ability to meet multiple deadlines, with a high degree of accuracy and…