|CCL 18.04.26 ***Full-time Genentech Opportunity***in silico ADME Scientist***South San Francisco, CA|
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To: jobs at ccl.net
Date: Thu Apr 26 03:07:42 2018
Subject: 18.04.26 ***Full-time Genentech Opportunity***in silico ADME Scientist***South San Francisco, CA
The department of Drug Metabolism and Pharmacokinetics (DMPK) at Genentech is seeking an exceptional candidate to support in silico ADME, guide DMPK SAR and influence chemical design. The candidate will build, apply and maintain in silico ADME models and tools to support discovery project teams. S/he will work with our established team to exploit and expand the existing modeling suite using state of the art computational chemistry and cheminformatics approaches (e.g. machine learning QSAR, matched molecular pairs, structure based design). Additionally s/he'll focus on predictive ADME and early Physiologically-Based PK models, linking in silico, in vitro and in vivo in a semi-automated fashion to inform molecular lead optimization via early human PK prediction. As part of the DMPK team s/he will explore ADME SAR, in vitro to in vivo correlations, leverage in silico and in vitro data to guide next steps for generating and testing experimental hypothesis as well as aid compound design. A key part of the role will be continuing education, both within DMPK and the small molecule organization, about predictive DMPK and championing the use of in silico tools. The candidate will be a highly effective and energizing collaborator and will work with scientists from multiple disciplines beyond DMPK including computational and medicinal chemistry, biochemical pharmacology, safety assessment and pharmaceutical sciences. Requirements: A PhD specializing in medicinal chemistry, computational chemistry, chemical engineering, or related fields. 0-4 years relevant industry experience in drug discovery. A strong understanding of organic chemistry and basic drug metabolism. Familiarity with applied statistical and data driven modeling techniques, such as machine learning and AI methods as well as mechanistic compartmental modeling. Hands on experience with programing languages (such as R, Python, Knime and Java), chemical data mining tools (e.g. Vortex) and molecular modeling tools (such as Molecular Discovery, Schrodinger, CCD:MOE). The successful candidate will be highly motivated, organized and detail oriented with excellent interpersonal and communication skills. Desired: A strong understanding of in vitro and in vivo DMPK principles. Experience with 3D structural modeling and familiarly with PK modeling and simulation platforms such as Simcyp, GastroPlus, Phoenix WinNonLin.
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