AI Computational Chemist – Peptide Design

Join AstraZenecas Hit Discovery R&D team as an AI Computational Chemist and help shape the future of drug discovery. We are building a new generation of AI-enabled computational tools to accelerate the discovery of innovative medicines and this role will play an important part in advancing our capabilities in peptide design.

This position is primarily focused on applying computational chemistry modelling and AI/ML to support the design and optimisation of peptides and related modalities across discovery programmes. You will contribute to projects from early concept through to candidate discovery helping to generate insights that guide molecule design prioritisation and experimental strategy.

You will join a global computational chemistry team spanning Sweden and the UK working at the interface of chemistry structural biology biophysics data science and discovery biology. The team supports AstraZenecas peptide portfolio across a broad range of therapeutic areas and is committed to combining scientific excellence with innovation in method this highly collaborative environment you will have the opportunity to make a meaningful impact across multiple programmes and to contribute to the evolution of our peptide design capabilities.

This position is based at our vibrant R&D site in Gothenburg Sweden.

What youll do:
As an AI Computational Chemist (formal title: Senior Scientist) you will apply advanced computational approaches to support peptide-focused drug discovery with particular emphasis on modelling design and analysis of complex datasets. You will contribute directly to project progression by generating high-quality computational insights and translating them into actionable recommendations for multidisciplinary teams.

A key part of the role will be helping to build and apply AstraZenecas future peptide design capabilities including the use of AI and machine learning to inform molecule design and decision-making. You will work with contemporary computational methods such as molecular dynamics free energy calculation approaches structure-based design and modern AI/ML techniques including co-folding selecting and applying the right methods to address project-specific questions.

You will also contribute to broader small molecule cheminformatics and virtual screening activities where these complement peptide discovery efforts or support wider hit-finding objectives. This aspect of the role is a valuable secondary component and will benefit from experience in data-driven compound prioritisation workflow development and computational support for early hit discovery.
Success in this role will require scientific independence creativity and a strong foundation in computer-aided drug design. You will be expected to communicate complex findings clearly influence project direction and work closely with experimental scientists to help shape strategy and guide follow-up studies.

Essential requirements:
Our team is a highly collaborative group of scientists working in an evolving technical and scientific landscape. Therefore you will be comfortable working in a dynamic and team-focussed environment. Critical will be to have effective communication skills and a proactive and delivery-focused approach. Aligned to Hit Discovery activities you will bring analytical insight innovation and a strategic mindset to drive peptide design through computational chemistry and cheminformatics in this exciting and evolving area.

You also have:

• A PhD in Computational Chemistry Cheminformatics Computer Science Bioinformatics Structural Biology AI/ML or another closely related discipline or bring equivalent research experience.

• Strong expertise in peptide modelling and design together with a clear track record of applying computational chemistry methods to solve drug discovery problems. We are looking for candidates with demonstrated interest and practical experience in the development or application of AI/ML methods ideally including generative approaches relevant to molecular design.

• Strong programming skills in Python including experience with relevant libraries and frameworks such as PyTorch TensorFlow or DeepChem and familiarity with modern optimisation methods and workflow tools.

• Hands-on experience with widely used computational chemistry software is also expected.

• Excellent communication collaboration presentation and influencing skills together with the ability to work effectively in a fast-paced multidisciplinary research environment.

Desirable requirements:

• Experience in biophysics structural modelling and methods for studying protein dynamics would be highly valuable. We would also welcome candidates with a strong record of applying multiple computer-aided drug design techniques in an industrial or highly applied research setting.

• Experience in cheminformatics and virtual screening would be advantageous particularly where it has been applied to hit finding library design compound prioritisation or workflow development. While this is not the primary focus of the role it would broaden the impact of the successful candidate across the wider activities of the team.

• A strong publication record together with experience of presenting scientific work at national and international conferences would also be viewed positively.

Work policy:
When we put people in the same room we unleash bold thinking with the power to inspire life-changing -person working gives us the platform we need to connect work at pace and challenge perceptions. Thats why we work on average a minimum of three days per week from the office. This role is located in Gothenburg Sweden and is not available for remote work or with travel or commuting support.

Why AstraZeneca:
There are many opportunities to develop yourself and your career. From our diverse portfolio and collaboration to our ground-breaking innovations its a place for lifelong learning.

Whats next:
If this sounds like the place and role for you apply today! We look forward to get to know you better! Welcome with your application no later than September 6 2026.

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