Computational Medicinal Chemist

Novartis • San Diego, CA

9d • Hybrid

About The Position

Join our vibrant and innovative Computer-Aided Drug Discovery (CADD) team in California, where we bring together diverse talents to redefine the way Biomedical Research discovery teams validate and develop new targets. We are a driving force behind drug discovery, and we are now eagerly searching for an exceptional computational scientist like you to join our ranks. Imagine the opportunity to unlock hidden knowledge and disruptive insights from the vast and invaluable data collected by one of the world's most renowned pharmaceutical companies. We need your expertise, experience, and unwavering passion to help us extract this wealth of information. Collaborating with a multidisciplinary group of scientists, you will be at the forefront of crafting inventive solutions to the most pressing drug discovery challenges, forging new paths toward groundbreaking medicines. Are you ready to seize this extraordinary chance to make a significant impact in the field of drug discovery? We invite you to embark on this thrilling journey with us, as we push the boundaries of what's possible in scientific exploration. Join our team and be part of a revolution that will shape the future of medicine. Together, we will transform the landscape of drug discovery, accelerate breakthroughs, and change lives. Apply now and let your expertise shine in our dynamic and forward-thinking environment.

Requirements

• PhD in medicinal chemistry, computational chemistry, computational biology, computational chemical biology, or a related field. Candidates with a laboratory-based background in chemistry and biology, supplemented with strong computational experience, are also encouraged to apply. We will also consider MS applicants with 6+ years of relevant Pharma/biotech experience.
• 3+ years of experience working with project teams in a drug discovery environment.
• Proven track record of innovation through analogue design, leading to significant impact on discovery projects.
• Familiarity with computational drug design tools (e.g. Schrdoinger, CCG, OpenEye, etc.), high-performance computing environments, and strong publication history in peer-reviewed journals.
• Proactively anticipates project needs with a clinical focus.
• Demonstrates rigor and diligence in idea substantiation, analogue design, and experimentation.
• Strong team orientation with multitasking and adaptability in support and leadership roles.
• Effective listener with excellent written and oral communication skills.
• Proficient in data visualization to effectively communicate insights.

Responsibilities

• Drive the design of medicinal chemistry efforts by applying in-depth knowledge of structure-activity relationships (SAR), structure-based and ligand-based drug design, a profound understanding of target biology, and predictive methods for assessing on- and off-target activity, physical properties, pharmacokinetics/pharmacodynamics (PK/PD), and synthetic feasibility.
• Thrive at the intersection of experimental and groundbreaking digital technologies, with a particular emphasis on expertise in machine learning, active learning and physics-based CADD methodologies as applied to small molecule drug discovery as well as in the induced proximity space.
• Stay abreast of scientific literature and engage with internal and external scientists to incorporate biological insights into lead characterization and screening initiatives.
• Collaborate with interdisciplinary project teams to facilitate effective decision-making throughout the target identification, lead optimization and drug candidate nomination process. This involves applying and developing predictive models based on high-content and time-resolved screening data, including imaging techniques.
• Drive hypothesis generation to enhance clinical success rates for programs involving small molecules, peptides, RNAs, protein degradation, molecular glues, transient covalent inhibitors, and kinetic stabilization of drug-target complexes.
• Take a leading role in cross-disciplinary mechanistic studies using physics-based modeling and simulation, biophysical characterization, and cellular validation. These studies will inform the strategic targeting strategies of discovery projects, aiming for optimal mechanisms of action (MoAs).

Benefits

• health, life and disability benefits
• a 401(k) with company contribution and match
• a variety of other benefits
• a generous time off package including vacation, personal days, holidays and other leaves

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree