You will have the opportunity to contribute to this high performing cross-functional team who seek to apply their knowledge in the high impact field of improving our capability in drug discovery.

We’ve assembled an exceptionally diverse, talented and spirited team who sincerely enjoy coming to work every single day to bring their ideas and real passion for new technology and medicine discovery. You will work alongside recognised thought leaders at the cross section of Machine Learning and Chemistry data, with plenty of interaction with drug discovery researchers.

You'll become a pioneer within a novel multidisciplinary field combining AI and drug discovery, be given autonomy and freedom to define your own role using state-of-the-art technologies within a supportive and meritocratic flat structure with a plethora of opportunities to build and develop your skills both internally and externally within a company that cares.

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• Support drug discovery projects at BenevolentAI manifesting the impact of our own technology platform to successfully progress these projects.
• Provide compound design support and data analysis for the Drug Discovery programmes undertaken by BenevolentAI.
• Be involved in advances and enhancements made to the Benevolent Platform™, identifying improvements and efficiency gains that can be made.
• Collaborate and communicate effectively with members of the Chemoinformatics, Bioinformatics, Drug Discovery, Artificial Intelligence, Data Science, Engineering, UX and Product teams to deliver BenevolentAI corporate strategic goals.

• PhD (or equivalent) in computational chemistry, molecular modelling, cheminformatics or a closely-related discipline relevant to drug discovery.
• Extensive experience applying computational chemistry and molecular modelling expertise to the successful advancement of drug discovery projects to important milestones.

• Prior experience of drug discovery project support, such as implementing compound library design, QSAR, docking, virtual screening, molecular fragmentation, structure-based drug design, pharmacophore generation and analysis, or multi-parameter optimisation.
• Experience of working with teams and collaborators across different disciplines and geographies.
• A solid understanding of chemical descriptors and property predictors, including their appropriate application and potential pitfalls.
• Experience with ligand- and structural-based virtual screening techniques (Docking (e.g. GLIDE, GOLD, AutoDock), pharmacophore screening, shape similarity screening).
• Experience in a range of computational chemistry software (e.g. Schrodinger DD suite, MOE, KNIME, Pipeline Pilot).

• Basic understanding of machine learning and artificial intelligence.
• Innovative ideas and approaches in the Chemoinformatics and Computational Chemistry fields of research, as demonstrated by appropriate publications, presentations, and code contributions to open source projects.
• Excellent communicator, especially when working with colleagues from other specialities.

Preferred (but not required) qualifications

• Track record of prioritising and initiating new projects and areas of research, integrating data and expertise from multiple sources.
• Experience of applying novel approaches to the process of lead identification and optimisation with the aim to significantly increase the overall efficiency of the journey from hypothesis generation to clinical testing. Examples of using novel data analyses or AI approaches in compound design or progression would be an advantage.
• Evidence of creative thinking to use novel strategies to move projects to important decision points quickly and efficiently.
• Evidence of continued learning and acquisition of new skills to constantly develop your drug discovery knowledge.

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