insitro

The Opportunity

Machine learning, integrated with data generation at scale, lies at the core of insitro’s approach to rethinking drug discovery and development. As Head of Molecular Design you will lead our small molecule medicinal chemistry efforts, helping us transform how drugs are designed, through the use of powerful predictive models, relating molecular structure to drug property, built through machine learning. You will couple these models with new ways of exploring and interrogating drug-like chemical space, achieving unprecedented advances in the speed and scale of hit discovery and lead optimization. You will drive our molecular design across a portfolio of therapeutic programs, delivering a stream of outstanding clinical candidates, and helping advance these into the clinic. You will work closely with world-class machine learning, data engineering, translational science and DNA-encoded library teams to grow our drug design capabilities and scale our therapeutic portfolio. You will partner with therapeutic teams to design and execute research project plans which reflect insitro’s data science-led philosophy for drug discovery. You will have demonstrated your ability to work in a highly cross-disciplinary, fast-moving and fluid environment, and will draw upon extensive experience driving drug discovery projects forward. You will possess strong interpersonal and leadership skills, an ability to lead cross-functionally, a strong collaborative work ethic, ability to adapt to changing needs, and the capacity to multitask to meet key goals. Strong communication and presentation skills are essential to success in this position.

Specifically, you will:

• Build and lead the insitro Molecular Design organization
• Drive all aspects of drug design across our portfolio of therapeutic projects
• Define a new approach to medicinal chemistry, enabled by machine learning and access to molecular property data at scale
• Partner with colleagues in machine learning, data engineering, and high throughput chemistry groups to develop and expand our suite of in silico drug design tools
• Partner with colleagues in arrayed screening and process engineering groups to enable selection of relevant compound sets for phenotypic screening programs
• Partner with colleagues in translational science to formulate and execute therapeutic project plans through to clinical candidate delivery and beyond
• Identify opportunities to apply machine learning to enhance our drug design environment through acquisition and integration of new drug property datasets into our computational pipelines
• Guide incorporation of structural biology capabilities and insights into our design paradigm
• Oversee significant external synthetic chemistry resources and ensure their efficient incorporation into our drug discovery processes
• Represent insitro in interactions with strategic partners in all drug design aspects of these collaborations
• Drive improvements in our overall drug discovery process, finding efficiencies at every stage of our pipeline

You will be joining the founding team of a biotech startup that has long-term stability due to significant funding, but yet is very much in formation. Much can change in this early and exciting phase, providing many opportunities for significant impact. You will work closely with an exceptionally talented team, learn a broad range of skills, and help shape insitro’s culture, strategic direction, and outcomes. Join us, and help make a difference to patients!

About You

• Ph.D. in a computational or synthetic chemistry  discipline, or equivalent experience
• 10+ years of relevant experience in the pharmaceutical/biotech industry
• An outstanding scientific track record in your area of expertise
• Deep understanding of the drug discovery process
• First-hand experience of leading multiple drug discovery and early-stage development projects
• First hand experience in setting up and using CROs for the drug discovery process
• Direct experience of building, scaling and leading a drug design organization
• Extensive knowledge of, and enthusiasm for, the use of in silico predictive models in drug design
• Direct experience with in silico predictive models, such as docking, FEP, QSAR & ML, in advancing discovery projects
• Familiarity with the application of machine learning methods to the drug design space
• Track record of discovering clinical candidates which have progressed to human clinical studies
• Working knowledge of pre-IND development phase
• Strong team/collaborative skills
• Demonstrated leadership abilities
• Strong written and verbal communication skills
• A desire to transform drug discovery and help patients
  

Benefits at insitro

• Excellent medical, dental, and vision coverage
• Open vacation policy
• Team lunches (catered daily)
• Commuter benefits
• Paid parental leave

About insitro

insitro is a data-driven drug discovery and development company using machine learning and high-throughput biology to transform the way that drugs are discovered and delivered to patients. The company is applying state-of-the-art technologies from bioengineering to create massive data sets that enable the power of modern machine learning methods to be brought to bear on key bottlenecks in pharmaceutical R&D. The resulting predictive models are used to accelerate target selection, to design and develop effective therapeutics, and to inform clinical strategy. insitro was launched in 2018 with a Series A of $100M funded by top investors including a16z, Arch Venture Partners, Foresite Capital, GV, and Third Rock Ventures.

The company has announced collaborations with Gilead Sciences in the area of NASH (2019) and Bristol Myers Squibb in the area of ALS (2020) and, in mid 2020, completed a Series B financing of $143M including current investors and new investors Canada Pension Plan Investment Board (CPP Investments), T. Rowe Price, BlackRock, Casdin Capital and other leading investors. The company is located in South San Francisco, CA. For more information about insitro, please visit the company’s website at www.insitro.com