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Senior Principal Cheminformatics Data Scientist

2 months ago

London, Greater London, United Kingdom BenevolentAI Full time
Job Title: Senior Principal Cheminformatics Data Scientist

We are seeking a highly experienced Senior Principal Cheminformatics Data Scientist to join our Cheminformatics & Computational Chemistry team at BenevolentAI. As a key member of our team, you will play a significant leadership role in advancing our small molecule drug discovery programmes.

Key Responsibilities:
  • Lead the cheminformatics and computational modelling support for multiple drug discovery projects, working closely with medicinal and computational chemists, and the rest of the project team.
  • Develop and deploy innovative approaches to expand our cheminformatics capabilities, and drive the long-term strategic thinking of the 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.
  • Gather, analyse and report on biochemical data from a range of data sources to derive novel insights into SAR and SPR, including the manipulation and analysis of biochemical data at scale.
  • 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.
  • Act as the key domain expert for cheminformatics and the handling of biochemical data, and consult with scientific and engineering teams from across BenevolentAI.
  • Collaborate and communicate effectively with members of the Chemoinformatics, Computational Chemistry, Bioinformatics, Drug Discovery, Artificial Intelligence, Engineering and Product teams.
  • Line-manage a portion of the team, defining and monitoring their individual goals, in line with company and department objectives, and conduct performance reviews.
  • Nurture talent at BenevolentAI by supporting junior members of the team in their working, sharing your experience and providing a mentoring role.
Requirements:
  • PhD or equivalent in Chemoinformatics, Computational Chemistry, Molecular Modelling or a closely related field and extensive experience of computer-aided drug discovery in pharma, biotech or academic drug discovery unit.
  • Detailed demonstrable knowledge of a wide range of chemoinformatics approaches and their application to live drug discovery projects, and the ability to objectively design scientifically-merited experiments.
  • Extensive practical experience of computer-aided drug design, such as compound library design, similarity and substructure searching, virtual screening, reaction enumeration, molecular fragmentation, R-group analysis and combinatorics, multi-parameter optimisation.
  • Practical experience in developing, deploying and applying machine learning and QSAR modelling techniques to chemical and biological data, and knowledge of a wide range of chemical featurisers, and a strong understanding of best practices.
  • Extensive experience processing chemical and biological data from a range of data sources, 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 and leadership skills, especially when working with junior colleagues from a range of technical and scientific backgrounds.
Desirable Skills:
  • Experience setting up and managing computational infrastructure for cheminformatics and computational chemistry applications.
  • 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.