PhD Student, Department of Chemistry and Clare Hall
BSc St. Stephen's College, University of Delhi
MPhil University of Cambridge
Srijit Seal is a researcher specializing in chemoinformatics. His research is centred on using machine learning techniques, particularly modelling and interpretation of the Cell Painting assay, to predict drug bioactivity, safety, and toxicity. Seal actively engages in academic outreach, promoting the understanding of Artificial Intelligence and delivering seminars on its applications in drug discovery.
Research
Predicting Cytotoxicity using Cell Painting data
We show that using Cell Painting data improved the performance of cytotoxicity models and we can further interpret descriptors related to nuclei texture, the granularity of cells, cytoplasm and cell neighbours and radial distributions among others. (https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.0c00303)
Predicting Mitochondrial Toxicity using Cell Painting and Gene Expression
We perform a detailed biological interpretation of Cell Painting features in the context of mitochondrial toxicity prediction. We further show that using Cell Painting, Gene Expression features and Morgan fingerprints significantly improves the applicability domain of structural models. (https://www.nature.com/articles/s42003-022-03763-5)
Similarity-based merger models
Designing fusion models of morphology and chemical space using a decision boundary based on their distance to the training set to improve the prediction of biological assay outcomes. (https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00723-x)
From Pixels to Phenotypes: Interpreting Cell Painting Features in BioMorph Space
Developing a data-based standardized and comprehensive framework for understanding the complex features in the Cell Painting assay from a biological perspective. (https://www.biorxiv.org/content/10.1101/2023.07.14.549031v1)
Reviews
Using chemical and biological data to predict drug toxicity
In this review, we discuss the various sources of information that can be used to better understand and predict a compound's toxicity, safety or biological activity, including using biological data such as gene expression and cell morphology. (https://www.sciencedirect.com/science/article/pii/S2472555222137147)
Current projects
Advancing the understanding of Cell Painting Representations
This work focuses on interpreting the Cell Painting numerical features using convolutional neural networks to predict toxicity assays such as proliferation, oxidative stress, apoptosis etc.
Transfer learning from structure to Cell Painting: A case study for Drug-Induced Liver Injury
This work applies transfer learning/graph-based approaches to Cell Painting datasets for the prediction of Drug-Induced Liver Injury.
[New] PKSmart
Human PK prediction, using chemical structure, and animal data, to build interpretable PK prediction models. (Beta version: https://pk-predictor.serve.scilifelab.se)
[New] Using Predicted in vivo studies and in vitro assay data to predict Drug Induced Liver Injury
We are repurposing in vitro and in vivo data to predict organ-level toxicity with interpretable machine learning. (Beta version: Coming soon!)
Grants
2022: Accelerate-C2D3 New Funding Programme to Help Deploy AI for Research and Innovation (as Principal Investigator leading a group of 5 others)— ~£14k to publish a review on Theoretical, Scientific, and Philosophical Perspectives on Biological Understanding in the age of Artificial Intelligence.
Awards
• Cambridge International Scholarship (2020-2023) — £140k over 3 years of PhD starting 2020.
• Additional funding from Jawaharlal Nehru Memorial Fund.
• Additional funding from Honorary Allen, Meek, and Read Fund
• Trinity Henry Barlow Scholarship— £1k in recognition of achievements from Trinity College.
• Royal Society of Chemistry Researcher Development Grant— ~SLAS Europe 2022 Conference and Exhibition.
• Student Poster Award— Best student poster at the SLAS Europe 2022 Conference and Exhibition.
• Tony B Awardee — ~SLAS Europe 2022 Conference and Exhibition.
• Clare Hall Progression Fund (2021) —towards a research presentation tour in India
• BP-Clare Hall India Innovation Masters Studentship (2019) — £45k for 1 year of MPhil.
• Boak Fund, Clare Hall (2021 and 2020 and 2022) —recognising and supporting outstanding research.
Teaching
Supervisor for Undergraduate Courses in Part IA Chemistry, University of Cambridge, UK
• Involves tutorials (220+ hours) in fundamental principles in molecular processes.
Demonstrator for Laboratory Courses in Part IA and IB Chemistry, University of Cambridge, UK
• Involves experiments (200+ hours) of organic chemistry, applications of spectroscopy and inorganic chemistry.
External Activities
• Fellow of the Cambridge Philosophical Society (2021- Life membership)
• Associate Fellow (AFHEA)Associate Fellow (AFHEA), Advance HE
• Associate Membership of the Royal Society of Chemistry (2021-)
• Former Trustee of Clare Hall, University of Cambridge (2021-22)
• Graduate Student Member, Society of Toxicology
• Graduate Student Member, British Toxicology Society
• Graduate Student Member, Society for Laboratory Automation and Screening (SLAS)