Remote Computational Structural Biology Jobs (NOW HIRING)

• 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's R&D functions

• Expertise with structure-based design methods to support drug discovery projects in the industry

• Contributes to the Computational Chemistry group's 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's 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's 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

• 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

The pay you should reasonably expect to receive is $53.26 - $77.21 per hour.

Decisions depend on various factors, such as primary work location, complexity and responsibility of role, job duties/requirements, and relevant experience and skills.

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