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Up Directory CCL 18.04.26 ***Full-time Genentech Opportunity***in silico ADME Scientist***South San Francisco, CA
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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
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