Data Scientist, Discovery Chemistry

1 Global Discovery Chemistry community working across 7 disease areas at the Novartis Institutes for BioMedical Research (NIBR) is seeking a very hardworking and motivated Computational Chemist / Data Scientist to join our Global Discovery Chemistry Department in Cambridge, MA. You will join an energizing and collaborative research organization, working alongside colleagues who are committed to improving human health through the discovery of transformative medicines.<br><br>The Computer-Aided Drug Discovery (CADD) team in Cambridge brings together diverse abilities in helping NIBR discovery teams validate and drug new targets. We are seeking a unique Computational Scientist to complement the team with the skills, experience, and passion to extract new knowledge and disruptive insights from the large and rich body of data collected by the one of the world's oldest pharmaceutical companies. Working with a variety of multi-disciplinary scientists, you will help devise creative solutions to drug discovery problems, and innovative paths to new medicines.<br><br>You will contribute to the science of computer-aided automation in the context of early medicinal chemistry by bringing together machine learning, and high-throughput chemistry solutions to a platform that coalesces data and experimentation to minimize discovery cycle time.<br><br>Your Key Responsibilities Include:<br><br>- Develop and apply computational strategies in our multidisciplinary lab space where synthesis, purification, analytics, ADME profiling, virtual screening, and data science work together to advance the science of lead discovery.<br><br>- Lead data driven medicinal chemistry design efforts by building knowledge of SAR from high-throughput experimentation, deep understanding of target biology, and application of predictive methods for on- and off-target activity, physical properties, PK/PD, and synthetic feasibility.<br><br>- Apply machine learning and modeling techniques to advance chemical hypotheses through virtual enumeration, 3D evaluation & docking, and active learning approaches to build and advance biological target understanding.<br><br>- Aggregate multiple modeling and compound evaluation approaches to distill deeper drug discovery insight and apply these insights to compound design efforts.<br><br>- Lead cross-disciplinary mechanistic studies using physics-based modeling and simulation, biophysical characterization, and cellular confirmation to direct project-targeting strategies toward optimal MoAs.<br><br>- Stay on top of scientific literature and interact with internal and external scientists to integrate biological insights into lead characterization and screening efforts.<br><br>- Collaborate with interdisciplinary project teams to drive effective decision-making from target identification through candidate nomination by mining and developing predictive models using high-content and time-resolved screening data, including imaging.<br><br>- Drive hypothesis generation to result in higher clinical success rates for programs using small molecules, peptides, RNAs, protein degradation, molecular glues, transient covalent inhibitors, and kinetic stabilization of drug-target complexes.<br><br>[#video#https://www.youtube-nocookie.com/embed/ggbnzRY9z8w{#400,300#}#/video#]