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Who We Are:
At Neurocrine Biosciences, we pride ourselves on having a strong, inclusive, and positive culture based on our shared purpose and values. We know what it takes to be great, and we are as passionate about our people as we are about our purpose - to relieve suffering for people with great needs, but few options.
What We Do:
Neurocrine Biosciences is a neuroscience-focused, biopharmaceutical company with a simple purpose: to relieve suffering for people with great needs, but few options. We are dedicated to discovering and developing life-changing treatments for patients with under-addressed neurological, endocrine and psychiatric disorders. The company's diverse portfolio includes FDA-approved treatments for tardive dyskinesia, Parkinson's disease, endometriosis* and uterine fibroids*, as well as clinical programs in multiple therapeutic areas. For three decades, we have applied our unique insight into neuroscience and the interconnections between brain and body systems to treat complex conditions. We relentlessly pursue medicines to ease the burden of debilitating diseases and disorders, because you deserve brave science. *in collaboration with AbbVie
About the Role:
Neurocrine is expanding our R&D chemistry capabilities. In this exciting new role, you will be instrumental in the success of our growing computational chemistry team. The successful candidate will be responsible for the execution of computational driven methodologies to help design optimized compounds with balanced properties (targets, DMPK, in-vivo) in drug discovery programs, that could range from early lead identification to late-stage optimization phase. Will be a member of multi-disciplinary drug discovery teams of medicinal chemists, DMPK, structural biologists and pharmacologists, where opportunities to impact will abound.
Experience with Molecular Modeling domains is required, as applied to compound design and optimization such as Pharmacophore Analyses, Library Design, virtual HTS, Diversity/Similarity Analyses, Scaffold Hopping. A demonstrated success with an overall application of several integrated approaches (ex: ML derived predictions, Modeling SBD/ LBD) to progressing compound design contextual in drug discovery, is highly desirable and will serve as a strong bonus to consideration. Publications, posters or documented examples would be helpful.
Preference also given to candidates with previous roles in biotech/pharma companies and capable of independently driving forward Drug Discovery projects involving Structure Based Design including, but not limited to, target protein flexibility considerations.
Exposure to harnessing large datasets including public domain datasets of chemistry related to various targets and/or chemogenomic nature would be an asset.
Knowledge about computational technologies for the assessment of early-stage targets (ex: druggability) is helpful but not essential. Familiarity with well-known commercial molecular modeling software suites is also desirable such as Schrodinger, CCG or Open Eye. _
**Please note this will be a 6 month contract**
Your Contributions (include, but are not limited to):
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
Requirements:
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
#LI-OB1 Neurocrine Biosciences is an EEO/AA/Disability/Vets employer. We are committed to building a diverse, equitable, and inclusive workplace, and we recognize there are a variety of ways to meet our requirements. We are looking for the best candidate for the job and encourage you to apply even if your experience or qualifications don't line up to exactly what we have outlined in the job description.
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Neurocrine Biosciences
Jan 28, 2025