DMPK Lead, London, Lausanne

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

The DMPK and Quantitative Pharmacology team, part of the Preclinical Development group, accelerates the delivery of high quality drug candidates by developing innovative and creative AI-first solutions. By applying our domain expertise and through collaboration with the entirety of our world-class drug discovery disciplines and ML researchers, we ensure that drug-like properties are at the heart of our drug design engine. We shape the development of state-of-the-art ADME and toxicology predictive models. We rethink drug discovery from Hit ID to first-in-human (FIH) trials by optimising the context of use of these models.

At Isomorphic Labs, DMPK Leads drive the DMPK strategy of drug discovery projects to deliver high quality clinical candidates whilst using and contributing to the optimisation of state-of-the-art computational and AI ADME models. 

As part of the Drug Design and Medical Research team and through close collaboration with the Technology teams, you will be supporting the business alongside world leading AI/ML Drug Design Platform teams. You will collaborate to deliver scientific expertise and strategic direction to drive both improvements in the Platform and apply this to real drug design projects. 

If you are a skilled and ambitious scientist looking to take the next step in your career, joining Isomorphic Labs will lift you out of your comfort zone and provide you with an amazing opportunity for personal growth and development.

• →Design and implement stage-appropriate DMPK strategies with AI-first approach, integrating quantitative pharmacology and overall project strategies.
• →Design screening cascade and mechanistic experiments to support project progression in a data-driven manner.
• →Integrate DMPK, PD and toxicological data to extract decision-making information, including dose and safety window predictions.
• →Define Quantitative Pharmacology strategy in collaboration with Pharmacology Leads.
• →Optimise the use of Iso’s datasets to address fundamental DMPK questions and advance the field.
• →Optimise molecule design based on physico-chemistry and biotransformation knowledge in collaboration with Chemistry Leads.
• →Maintain knowledge of state-of-the-art technologies and methods to increase confidence in human PK prediction.
• →Author DMPK sections of regulatory documents.
• →Embrace and champion a culture of inclusion and continuous professional development.

• PhD in either Pharmaceutical Science, Pharmacy, Chemistry, or another relevant subject.
• 3+ years’ professional experience as a DMPK Lead, solving DMPK-related problems in drug discovery with a track record of working collaboratively to deliver high quality candidates.
• Practical experience developing DMPK and quantitative pharmacology packages to support early clinical development, including authoring and reviewing regulatory documents, ideally in oncology or immunology.
• Experience of partnering closely with Preclinical and Clinical teams.
• In depth knowledge of biotransformation.
• In depth knowledge of PK modelling and scaling of ADME properties.

• Experience with biologics, peptides and other new modalities.
• Advanced knowledge of PKPD modelling.
• Good understanding of machine learning techniques.
• Familiarity with Python and KNIME.

We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. 

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