Senior Principal Research Scientist, Computational Chemistry

About the role

IDEAYA is a precision medicine oncology company dedicated to discovering, developing, and commercializing transformative therapies for cancer. Our approach combines expertise in small-molecule drug discovery, structural biology, and bioinformatics with internal capabilities in identifying and validating translational biomarkers to develop tailored, potentially first-in-class targeted therapies aligned with the genetic drivers of disease.

Responsibilities

• Lead structure-based drug design meetings with medicinal chemists and provide senior-level, hands-on drug design and computational chemistry support to small molecule drug discovery project teams
• Contribute to programs from target identification through lead optimization and drug candidate selection
• Apply structure-, ligand-, and fragment-based modeling to guide medicinal chemistry design
• Use molecular dynamics, free energy methods, quantum chemistry, and cheminformatics to address complex design and prioritization questions
• Translate computational analyses into clear, actionable compound design recommendations
• Serve as a highly experienced computational contributor and scientific advisor within project teams
• Mentor and coach other scientists in structure-based drug design through technical guidance and scientific discussion
• Evaluate new computational methods, tools, and workflows and apply them when they add clear value

Requirements

• PhD in Computational Chemistry or a related discipline with 9+ years of pharmaceutical or biotechnology industry experience
• Strong understanding of small molecule drug discovery and medicinal chemistry principles
• Demonstrated expertise in structure-, ligand-, and fragment-based drug discovery
• Expertise in free energy perturbation, molecular dynamics, quantum mechanics, cheminformatics, and ligand affinity estimation methods
• Proven ability to independently deliver computational work that influences medicinal chemistry decisions and project outcomes
• Able to manage priorities across multiple discovery programs
• Excellent oral and written communication skills
• Experience applying machine learning or AI methods to molecular design, property prediction, or compound prioritization
• Experience with Schrödinger modeling software and others (e.g., MOE, OpenEye)
• Programming, scripting, statistics, and data analysis experience applied to drug discovery
• Experience working on challenging target classes, including protein–protein interactions and allosteric pockets
• Track record of contributing to discovery programs that advance high-quality small molecule drug candidates

Qualifications

PhD in Computational Chemistry or a related discipline with 9+ years of pharmaceutical or biotechnology industry experience.