Sr. Scientist, Computational Chemistry

Neurocrine Biosciences • San Diego, CA

20h • Onsite

About The Position

Neurocrine Biosciences is a leading biopharmaceutical company dedicated to discovering, developing, and commercializing life-changing treatments for patients with under-addressed neurological, psychiatric, endocrine, and immunological disorders. The company's diverse portfolio includes FDA-approved treatments and a robust pipeline. This role is 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. The position provides impactful insights and collaboration on projects ranging from early lead identification to the late-stage optimization of advanced projects. The Sr. Scientist will serve as a subject matter expert in one or more molecular discovery approaches such as Structure-based Design & FEP, Virtual Screening, Quantum Chemistry, and Machine Learning/Modern AI. Responsibilities include the communication and presentation of computationally derived results to discovery project teams to facilitate effective decision-making, demonstrating an independent work style while being fully collaborative and team-oriented.

Requirements

• 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 efficiency.
• Strong project management skills.
• A collaborative & team-oriented mindset is essential.

Nice To Haves

• Prior experience with independently driving drug discovery projects is highly desired for this role.
• Domain knowledge of most or all of 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.
• 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.
• 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.
• Familiarity with well-known commercial molecular modeling software suites is also desirable such as Schrodinger, CCG or Open Eye.

Responsibilities

• Driving the execution of computational driven methodologies to help design optimized compounds with balanced properties (targets, DMPK, in-vivo) in drug discovery programs.
• Providing impactful insights and collaboration on projects ranging from early lead identification to the late-stage optimization of advanced projects.
• Serving 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.
• Communicating and presenting computationally derived results to the discovery project teams to facilitate effective decision-making.
• Demonstrating an independent work style while being fully collaborative & team-oriented.
• Prior experience with independently driving drug discovery projects is highly desired.
• Developing advanced Machine Learning/AI in-silico models for modeling DMPK/in-vitro Biology endpoints, for front-loading projects with appropriate predictive information, & enabling more efficient MPO analyses & new compound designs.
• Harnessing large datasets including public domain datasets of chemistry related to various targets and/or chemogenomic nature.
• Demonstrating 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.
• Serving as an independent Comp Chem representative on Project teams, and working with minimal additional guidance, while demonstrating clear impact on project’s chemical series evolution.
• Advancing 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.
• Leading 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.
• Engaging stakeholders from multiple Research functions to deliver and/or exchange key results.
• Driving and/or aligning with strategies emanating from project teams, department and computational chemistry group.
• Providing training and/or supervision to junior staff, as needed.
• Other duties as assigned.

Benefits

• retirement savings plan (with company match)
• paid vacation, holiday and personal days
• paid caregiver/parental and medical leave
• health benefits to include medical, prescription drug, dental and vision coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior