TEMP - Sr. Scientist, Computational Chemistry (6 month contract)

Role Summary

TEMP - Sr. Scientist, Computational Chemistry (6 month contract).

Responsibilities

• Projects could range from early lead identification to the late-stage optimization of advanced projects. In particular, you will be able to join and potentially lead the development of an in-silico modeling platform within the Chemistry Department. As an active contributing member of multi-disciplinary drug discovery projects comprised of Medicinal Chemists, Biologists, DMPK & toxicologists there will be enormous opportunities to impact projects, as well as ample collaboration opportunities to share and learn from similar ML-derived predictive modeling efforts in other Neurocrine‚Äôs R&D functions
• Expertise with structure-based design methods to support drug discovery projects in the industry
• Contributes to the Computational Chemistry group‚Äôs efforts in implementing computational chemistry and/or cheminformatics methods for expediting the Design-Make-Test-Analyze discovery cycle
• Generates productive hypotheses from Protein-ligand docking, for project teams that leads to successful compound optimization in subsequent design cycles
• Develops advanced Machine Learning/AI in-silico models for numerous DMPK/in-vitro Biology endpoints, for front-loading projects with appropriate predictive information, to enable more efficient MPO analyses
• Takes ownership of predictive platform and provides maintenance including regular updates
• Facilitate the medicinal chemists design new compounds with desirable optimizable properties that are predicted using cutting-edge computational technologies integrating structural, chemical and biological data
• Employs computational platform to make significant contribution to rationalizing experimental results, SAR evolution, and generating impactful ideas that are aligned with team‚Äôs strategy to progress compounds forward in projects
• Plays a lead role in identifying and/or developing/refining new computational methods, in tandem with self-interest and relevance to projects, to help augment Neurocrine‚Äôs Computational Chemistry platform for Drug Discovery
• Participates in a multidisciplinary team committed to the continuous improvement of the lead optimization process as well as the expeditious identification of development compounds.
• Engages stakeholders from multiple Research functions to deliver and/or exchange key results
• May contribute to the assessment of early-stage projects to help determine its entry into portfolio
• Keeps abreast of developments of related interest through literature and advises project teams and/or computational chemistry group of innovation that could be harnessed into improving our platform
• Aligned with strategies emanating from project teams, department and computational chemistry group
• Conducive to sharing knowledge, practices, and work details, as needed, with teams and receptive to incorporating ideas from teams for continuous enrichment to best practices
• Other duties as assigned

Qualifications

• BS/BA degree in Chemistry and 5+ years of relevant experience, including familiarity utilizing any or all of the following: Machine Learning/AI based predictive modeling, Cheminformatics, Protein-Ligand modeling is preferred OR
• MS/MA degree in Chemistry and 3+ years of similar experience noted above OR
• 3+ years of post-Ph.D experience preferred
• Recognizes fundamental anomalies in data points and identifies issues in experiments / processes
• Begins to understand how to think outside of the technical process and consider the impact decisions will have on the broader scientific goals
• Strong knowledge of scientific discipline
• Good knowledge of scientific principles, methods and techniques
• Good knowledge and demonstrated ability working with a variety of laboratory equipment/tools
• Strong computer skills
• Good problem-solving, analytical thinking skills
• Detail oriented
• Ability to meet deadlines
• Excellent communication skills with the ability to collaborate with cross-functional scientists

Skills

• Structure-based design
• Computational chemistry and cheminformatics
• Machine Learning / AI in-silico modeling
• Protein-ligand docking
• Data analysis and predictive modeling
• Collaborative communication across functions

Education

• BS/BA or MS/MA or Ph.D. in Chemistry or related field (as per qualifications above)