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Data-Driven Drug Discovery and Molecular Informatics

BSc St. Stephen's College, University of Delhi

MPhil University of Cambridge

PhD Student, University of Cambridge

Srijit Seal is a researcher specializing in chemoinformatics. His research is centered on using machine learning techniques, particularly modeling 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.

Srijit Seal

Research

For an updated list see: Google Scholar

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 neighbors 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. (broad.io/BioMorphhttps://www.molbiolcell.org/doi/pdf/10.1091/mbc.E23-08-0298)

PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules

Human PK prediction, using chemical structure, and animal data, to build interpretable PK prediction models. (https://broad.io/PKSmart)

Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

We are repurposing in vitro and in vivo data to predict organ-level toxicity with interpretable machine learning. (https://broad.io/DILIPredictor)

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank (http://broad.io/DICTrank_Predictor, https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c01834) 

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability (http://broad.io/BioMorph, https://www.molbiolcell.org/doi/full/10.1091/mbc.E23-08-0298)

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. 

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 and SLAS US 2024 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)

Publications

PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules
S Seal, M-A Trapotsi, V Subramanian, O Spjuth, N Greene, A Bender
(2024)
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank
S Seal, O Spjuth, L Hosseini-Gerami, M García-Ortegón, S Singh, A Bender, AE Carpenter
– Journal of chemical information and modeling
(2024)
64,
1172
Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity
RI Horne, J Wilson-Godber, A González Díaz, ZF Brotzakis, S Seal, RC Gregory, A Possenti, S Chia, M Vendruscolo
– Journal of chemical information and modeling
(2024)
64,
590
Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data
S Seal, DP Williams, L Hosseini-Gerami, O Spjuth, A Bender
(2024)
From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability
S Seal, J Carreras-Puigvert, S Singh, AE Carpenter, O Spjuth, A Bender
– Mol Biol Cell
(2024)
35,
mr2
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.
S Seal, O Spjuth, L Hosseini-Gerami, M García-Ortegón, S Singh, A Bender, AE Carpenter
(2023)
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
S Seal, J Carreras-Puigvert, A Carpenter, O Spjuth, A Bender
(2023)
Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data.
S Seal, H Yang, M-A Trapotsi, S Singh, J Carreras-Puigvert, O Spjuth, A Bender
– J Cheminform
(2023)
15,
56
Using chemical and biological data to predict drug toxicity.
A Liu, S Seal, H Yang, A Bender
– SLAS Discovery
(2023)
28,
53
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection.
S Seal, J Carreras-Puigvert, M-A Trapotsi, H Yang, O Spjuth, A Bender
– Commun Biol
(2022)
5,
858
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Telephone number

01223 336432

Email address

College

Clare Hall