Computational Medicinal Chemist

About the role

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.

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).

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. Schrödinger, 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.

Qualifications

• PhD in medicinal chemistry, computational chemistry, computational biology, computational chemical biology, or a related field.
• MS 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. Schrödinger, 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.

Skills

• 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.
• Experience with computational drug design tools (e.g. Schrödinger, CCG, OpenEye, etc.).
• High-performance computing environments.
• Strong publication history in peer-reviewed journals.
• Ability to anticipate project needs with a clinical focus.
• 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.
• Proficiency in data visualization.

Benefits

• The salary for this position is expected to range between $126,000 and $234,000 USD annually for Senior Expert I, Data Science, and $138,600 and $257,400 USD annually for Senior Expert II, Data Science.
• The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically.
• Novartis may change the published salary range based on company and market factors.
• Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.
• US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits.
• In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.

Pay

• $126,000 - $234,000 USD annually for Senior Expert I, Data Science
• $138,600 - $257,400 USD annually for Senior Expert II, Data Science

Schedule

• Hybrid work schedule