Principal Cheminformatics Data Scientist- BenevolentAI Cambridge

Full time @Data Science Career in Data Science , in Data Scientist
  • Apply Before : December 18, 2023
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Job Detail

  • Job ID 21165
  • Sector Data Science

Job Description

We are looking for an experienced Principal Cheminformatics Data Scientist, with a keen interest in small molecule drug design, to join our Cheminformatics & Computational Chemistry team.

The Cheminformatics & Computational Chemistry team is a high performing cross-functional team that seeks to apply their knowledge to a diverse range of programmes from Target Identification through Hit ID, Hit Expansion and Lead Optimisation. Our role is to aid the advancement of our small molecule Drug Discovery programmes by devising computational solutions to project-specific challenges and applying new and existing technologies to support the needs of our wider portfolio.

As a Principal Cheminformatics Data Scientist within the team, you will contribute to this challenge by applying your cheminformatics, data analysis and computational modelling skills to advance our small molecule drug discovery programmes. You will work closely with medicinal and computational chemists to develop data and modelling pipelines, identify and apply innovative technologies, and employ state of the art computer-aided drug design techniques.



  • Lead the cheminformatics and computational modelling support for several drug discovery projects, working closely with medicinal and computational chemists, and the rest of the project team.
  • Apply a wide range of computer-aided drug design techniques to identify and develop small molecules, including virtual screening, reaction and fragment enumeration, de novo design, and chemical library design and sampling.
  • Build, evaluate and deliver QSAR models to advance our small molecule Drug Discovery programmes, and to support their use by project teams.
  • Develop processes, customisable workflows and computational techniques that can be adapted and applied across the drug discovery portfolio.
  • Collaborate and communicate effectively with members of the Chemoinformatics, Computational Chemistry, Bioinformatics, Drug Discovery, Artificial Intelligence, Engineering and Product teams.
  • Contribute to the development of our technical cheminformatics capabilities and help define the long-term strategic thinking of the computational team.
  • Nurture talent at BenevolentAI by sharing experience and offering a mentoring and/or line-management role, where appropriate


We are looking for:

Essential Skills:


  • PhD or equivalent in Chemoinformatics, Computational Chemistry, Molecular Modelling or a closely related field, with experience of computer-aided drug discovery in pharma, biotech or academic drug discovery unit
  • Strong and demonstrable knowledge of chemoinformatics approaches and their application to live drug discovery projects, and the ability to objectively design scientifically-merited experiments.
  • Practical experience of computer-aided drug design, such as compound library design, docking, virtual screening, molecular fragmentation, structure-based drug design, pharmacophore generation and analysis, multi-parameter optimisation
  • Practical experience in developing, deploying and applying QSAR models for small molecule drug discovery, and a strong understanding of best practices
  • Practical experience processing and deriving novel insights from large chemical data resources, e.g. ChEMBL, SureChEMBL, and PubChem
  • Strong and demonstrable programming and technical skills, and familiar with open source and proprietary Chemoinformatics libraries e.g. RDKit or other leading industry toolkits.
  • Innovator of new ideas and approaches in the Chemoinformatics and Computational Chemistry fields of research, as demonstrated by appropriate papers, presentations, or code contributions to open source projects.
  • Excellent communication skills, especially when working with colleagues from other specialities.

Desired Skills:

  • Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch), and state-of-the art ML approaches.
  • Familiarity with 3D ligand- and structural-based modelling techniques, such as docking, pharmacophore modelling, shape similarity screening, molecular dynamics simulations, water-site analysis and/or FEP analysis
  • Familiarity with modern software development paradigms, including containerisation with Docker, GitOps, and cloud computing on AWS with Kubernetes

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