Join the Ballester group at Imperial College London as a Research Associate and work on an exciting project aimed at developing the next generation structure-based virtual screening methods. Artificial intelligence (AI) and domain knowledge is a powerful alliance to discover molecules with the potential to become drugs for the considered disease. These models can leverage training datasets to identify such drug leads by computational (virtual) screening of massive libraries of molecules. In particular, AI models can be trained on atomic-resolution structures of macromolecular targets and the activities of their ligand molecules to predict the activities of other molecules across targets. Despite important successes, there are major challenges limiting the potential of such AI models. Some are specific to this problem (. how to augment training datasets in a way that improves the performance of these models). Other challenges are common to other supervised learning problems (. anticipating how well the models performs outside its applicability domain). The project includes prospective application of the developed methods with collaborators at the University of Cambridge. Some of the recent publications of the Ballester group in this area are:

Gómez-Sacristán P., Simeon S., Tran-Nguyen V-K., Patil S., Ballester . (2024) “Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers”. Journal of Advanced Research (In Press). Tran-Nguyen, V., Junaid, M., Simeon, S., Ballester, . (2023) “A practical guide to machine-learning scoring for structure-based virtual screening”. Nature Protocols 18, 3460–3511. Ballester PJ. (2023) “The AI revolution in chemistry is not that far away”. Nature 624:252. Li H., Sze K-H., Lu G., Ballester . (2021) “Machine-learning scoring functions for structure-based virtual screening”. WIREs Computational Molecular Science

Conduct the planned research of this EPSRC project and associated duties such as maintaining data security, and reporting research activities both internally and externallyPrepare the results for publication in high-quality peer-reviewed journalsPresent these findings at national/international conferencesCollaborate with other scientists, including other group members, as indicated by the project PI.Take an active and creative role in suggesting the next steps of the project and ensure rigorous research documentation and data collection, management, and interpretationUphold and promote the reputation of the Group, Department, and CollegeContribute to bids for research grants and actively participate in the research program of the groupAssist in the supervision of undergraduate and postgraduate research students and research assistants as required and comply with the College, Division, and Unit safety practices.Perform any other duties as deemed reasonable by the group leader.
Have a masters degree (or equivalent) in (machine learning, data science, chemoinformatics, bioinformatics, medicinal chemistry, or closely-related research areas).Practical experience within a research environmentLead-author publications in relevant and refereed journalsPractical experience in developing and evaluating machine learning modelsPractical experience in modelling protein structures and chemical structuresPractical experience in using high-performance computing (HPC) systemsPractical experience in curating and analysing bioactivity/binding dataExperience of working with and supervising students on undergraduate and/or masters level projects
The opportunity to continue your career at a world-leading institution Sector-leading salary and remuneration package (including 38 days off a year)