Open role
Head of Computational-Aided Drug Discovery
- Aggregated by CV-Library
- Cambridge
- Permanent
- Full Time
- £100,000 - £100,000 Per Annum
Job Description
Up to £100,000
Hybrid based in Cambridge
About the company
Our client is a well established, science led drug discovery company with a strong track record of delivering pre-clinical candidates for partners on challenging targets. They combine deep in-house structural and biophysical expertise (X-ray crystallography, NMR and a full suite of biophysical methods) with modern computational chemistry, cheminformatics, machine learning and AI, including structure enabled and fragment based approaches to hit and lead generation.
They are now looking for an outstanding computational scientist to lead their computational group and shape how modern computational methods are applied across the drug discovery portfolio.
The role Reporting to a senior chemistry leader, this is a senior scientific leadership position with genuine influence over how computational insight translates into project progress for the business and its partners. It is a hands-on role: you will set the scientific direction and standards for computational science, line manage and grow a small, growing team of computational chemists and data scientists, and stay closely embedded in live projects, applying the methods yourself where it matters most.
You will work alongside medicinal, synthetic and analytical chemists, protein scientists, structural biologists, biophysicists and biologists, guiding projects from hit identification through to candidate nomination.
What you'll be doing
Leading, directing and developing the computational chemistry and data science group, setting scientific strategy and best practice standards
Acting as the computational lead embedded within multidisciplinary project teams, generating and prioritising design ideas, resolving SAR and accelerating lead optimisation
Exploiting structure enabled and fragment based methodology, integrating experimental structural and biophysical data with modelling and design
Championing the appropriate use of machine learning, generative design and AI in drug discovery, including through collaborative AI driven drug discovery initiatives
Overseeing cheminformatics and data science workflows that support robust, data driven decision making at every stage of a project
Leading the analysis and exploitation of large scale screening and structural datasets to turn them into actionable insight for hit identification and design
Directing the selection, development and deployment of computational tools and workflows, both commercial and in-house
Line managing, mentoring and growing the team, recruiting as it expands
Representing computational science to partners and collaborators, building confidence in the science behind delivery
Keeping abreast of scientific and technological advances and driving adoption of new methods where they genuinely strengthen discovery outcomesWhat we're looking for
PhD (or equivalent) in computational chemistry, computer-aided drug discovery, cheminformatics or a related discipline
Significant, demonstrable experience of computational drug discovery gained within a biotech or pharma environment, not solely academic, including experience leading or directing computational work
Deep, hands-on expertise across core computational chemistry methods: molecular docking, virtual screening, molecular dynamics and quantum mechanical calculations
Practical experience applying machine learning and modern AI approaches to real drug discovery problems, with a clear understanding of their strengths and limitations
A demonstrable track record of using computational methods to help drug discovery project teams succeed, with tangible contributions to lead optimisation and candidate nomination
A strong track record of using experimental structural information (X-ray, ideally NMR) and biophysical data to drive structure based molecular design
Proven ability to lead and develop scientists, set direction and manage the priorities and delivery of a technical group
Excellent communication and interpersonal skills, with the ability to explain complex computational science to multidisciplinary teams and external partnersNice to have
Hands-on experience of computational approaches within a fragment based drug discovery setting
Experience with free energy perturbation (FEP) and other advanced predictive methods for potency and selectivity
Strong cheminformatics capability, including building or deploying analysis workflows for the wider discovery team
Experience analysing large or high dimensional screening datasets using big data and statistical methods
Programming and scripting ability (e.g. Python) and experience developing in-house tools or automating computational workflows
Experience across multiple target classes such as kinases, protein to protein interactions, GPCRs and ion channels
Familiarity with modalities beyond classical small molecules, such as PROTACs, RIPTACs and ADCs
Experience of collaborative, partnered or CRO style discovery, delivering to external stakeholders
A record of contribution to the field through publications, patents or presentations
Hybrid based in Cambridge
About the company
Our client is a well established, science led drug discovery company with a strong track record of delivering pre-clinical candidates for partners on challenging targets. They combine deep in-house structural and biophysical expertise (X-ray crystallography, NMR and a full suite of biophysical methods) with modern computational chemistry, cheminformatics, machine learning and AI, including structure enabled and fragment based approaches to hit and lead generation.
They are now looking for an outstanding computational scientist to lead their computational group and shape how modern computational methods are applied across the drug discovery portfolio.
The role Reporting to a senior chemistry leader, this is a senior scientific leadership position with genuine influence over how computational insight translates into project progress for the business and its partners. It is a hands-on role: you will set the scientific direction and standards for computational science, line manage and grow a small, growing team of computational chemists and data scientists, and stay closely embedded in live projects, applying the methods yourself where it matters most.
You will work alongside medicinal, synthetic and analytical chemists, protein scientists, structural biologists, biophysicists and biologists, guiding projects from hit identification through to candidate nomination.
What you'll be doing
Leading, directing and developing the computational chemistry and data science group, setting scientific strategy and best practice standards
Acting as the computational lead embedded within multidisciplinary project teams, generating and prioritising design ideas, resolving SAR and accelerating lead optimisation
Exploiting structure enabled and fragment based methodology, integrating experimental structural and biophysical data with modelling and design
Championing the appropriate use of machine learning, generative design and AI in drug discovery, including through collaborative AI driven drug discovery initiatives
Overseeing cheminformatics and data science workflows that support robust, data driven decision making at every stage of a project
Leading the analysis and exploitation of large scale screening and structural datasets to turn them into actionable insight for hit identification and design
Directing the selection, development and deployment of computational tools and workflows, both commercial and in-house
Line managing, mentoring and growing the team, recruiting as it expands
Representing computational science to partners and collaborators, building confidence in the science behind delivery
Keeping abreast of scientific and technological advances and driving adoption of new methods where they genuinely strengthen discovery outcomesWhat we're looking for
PhD (or equivalent) in computational chemistry, computer-aided drug discovery, cheminformatics or a related discipline
Significant, demonstrable experience of computational drug discovery gained within a biotech or pharma environment, not solely academic, including experience leading or directing computational work
Deep, hands-on expertise across core computational chemistry methods: molecular docking, virtual screening, molecular dynamics and quantum mechanical calculations
Practical experience applying machine learning and modern AI approaches to real drug discovery problems, with a clear understanding of their strengths and limitations
A demonstrable track record of using computational methods to help drug discovery project teams succeed, with tangible contributions to lead optimisation and candidate nomination
A strong track record of using experimental structural information (X-ray, ideally NMR) and biophysical data to drive structure based molecular design
Proven ability to lead and develop scientists, set direction and manage the priorities and delivery of a technical group
Excellent communication and interpersonal skills, with the ability to explain complex computational science to multidisciplinary teams and external partnersNice to have
Hands-on experience of computational approaches within a fragment based drug discovery setting
Experience with free energy perturbation (FEP) and other advanced predictive methods for potency and selectivity
Strong cheminformatics capability, including building or deploying analysis workflows for the wider discovery team
Experience analysing large or high dimensional screening datasets using big data and statistical methods
Programming and scripting ability (e.g. Python) and experience developing in-house tools or automating computational workflows
Experience across multiple target classes such as kinases, protein to protein interactions, GPCRs and ion channels
Familiarity with modalities beyond classical small molecules, such as PROTACs, RIPTACs and ADCs
Experience of collaborative, partnered or CRO style discovery, delivering to external stakeholders
A record of contribution to the field through publications, patents or presentations