PKPD Modelling Lead, London
Isomorphic LabsLondonPosted 26 March 2026
Tech Stack
Job Description
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease.
The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery.
Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture.
The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you.
About Iso
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.
Your impact
As the PKPD Lead, you will develop the quantitative pharmacology 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 drug design models. As part of the DMPK and Quantitative Pharmacology team, you will champion the use of model-informed drug discovery and development on both small molecule and biologics projects. You will work closely with the Biologics Discovery team to further develop AI models for biologics developability.
If you are an experienced and ambitious scientist looking to play an active part in the AI-driven transformation of the pharmaceutical industry, joining Isomorphic Labs will lift you out of your comfort zone and provide an amazing opportunity for personal growth and development.
What you will do
PKPD Lead
Design and implement quantitative pharmacology strategies with an AI-first approach, from target identification to pre-clinical development.
Provide scientific leadership and integrate PK and quantitative pharmacology strategies for biologics projects, within the broader project strategy. As a member of the project core team, you will collaborate closely with colleagues in Drug Design, Pharmacology, ML Engineering, and Preclinical and Clinical Development to resolve PK and PD challenges.
Develop advanced PKPD models for biologics and small molecule projects in collaboration with the DMPK and Pharmacology Leads.
Contribute to the choice of modality that is the most appropriate for each target. Develop the target molecule profile at the inception of the project.
Integrate PK, PD and toxicological data to extract decision-making information through mechanistic and empirical models, including dose and safety margin predictions for biologics projects.
Author PKPD sections of regulatory documents.
ML interactions
Partner with Machine Learning colleagues to integrate PK and PD aspects of the drug design platform and identify, generate and analyse data from which to develop and improve predictiv ... (truncated, view full listing at source)
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