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. 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 portfolio includes FDA-approved treatments for tardive dyskinesia, chorea associated with Huntington's disease, congenital adrenal hyperplasia, endometriosis* and uterine fibroids*, and a pipeline with compounds in clinical development. For more information, visit neurocrine.com, and follow us on LinkedIn, X, and Facebook. (*in collaboration with AbbVie) About the Role: Neurocrine is expanding our R&D chemistry capabilities. This role involves executing computational methodologies to design optimized compounds in drug discovery, from early lead identification to late-stage optimization. You will be part of multidisciplinary teams, impacting projects across medicinal chemistry, DMPK, structural biology, and pharmacology. Experience with Molecular Modeling, Pharmacophore Analyses, Library Design, virtual HTS, Diversity/Similarity Analyses, and Scaffold Hopping is required. Success with integrated approaches like ML predictions and modeling for compound design is highly desirable. Prior experience in biotech/pharma and knowledge of structure-based design, target druggability assessments, and commercial modeling software (Schrodinger, CCG, OpenEye) are preferred. This is a 6-month contract. Your Contributions: Lead and support drug discovery projects from early lead identification to optimization. Develop and refine in-silico modeling platforms and computational methods. Generate hypotheses from protein-ligand docking for compound optimization. Develop ML/AI in-silico models for DMPK and biological endpoints. Maintain and update predictive platforms. Assist medicinal chemists in designing compounds with desirable properties. Use computational platforms to rationalize experimental results and SAR evolution. Identify and develop new computational methods to enhance the platform. Collaborate across multidisciplinary teams and contribute to project assessments. Stay informed on technological advancements and share insights. Perform other duties as assigned. Requirements: BS/BA in Chemistry with 5+ years or MS/MA with 3+ years of relevant experience, or Ph.D. with 3+ years experience. Experience with Machine Learning, Cheminformatics, Protein-Ligand modeling preferred. Strong problem-solving skills, attention to detail, and ability to meet deadlines. Excellent communication skills for cross-functional collaboration.
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