TEMP - Senior Scientist, Computational Chemistry (Remote, Hybrid, or San Diego)

About the role

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. What We Do: Neurocrine Biosciences is a leading neuroscience-focused, biopharmaceutical company with a simple purpose: to relieve suffering for people with great needs. We are dedicated to discovering and developing life-changing treatments for patients with under-addressed neurological, neuroendocrine and neuropsychiatric disorders. The company's diverse portfolio includes FDA-approved treatments for tardive dyskinesia, chorea associated with Huntington's disease, classic congenital adrenal hyperplasia, endometriosis* and uterine fibroids,* as well as a robust pipeline including multiple compounds in mid- to late-phase clinical development across our core 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. For more information, visit neurocrine.com , and follow the company on LinkedIn , X and Facebook . ( *in collaboration with AbbVie ) 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. _ Your Contributions (include, but are not limited to): 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 sig