Neurocrine Biosciences is looking to expand its R&D chemistry capabilities by hiring a computational chemist to join their growing team. The goal is to design optimized compounds with balanced properties for drug discovery programs, ranging from early lead identification to late-stage optimization.
Requirements
- 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.
- 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.
- Machine Learning/AI based predictive modeling, Cheminformatics, Protein-Ligand modeling is preferred
Responsibilities
- Projects could range from early lead identification to the late-stage optimization of advanced projects.
- 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
Other
- Will be a member of multi-disciplinary drug discovery teams of medicinal chemists, DMPK, structural biologists and pharmacologists, where opportunities to impact will abound.
- 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
- 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
