đź“Ť Cambridge, MA, US
đź“Ť Cambridge, UK

Hello, I'm Srijit Seal

I am a researcher in chemoinformatics, centered on using machine learning techniques, particularly modeling, and interpretation of the Cell Painting assay, to predict drug bioactivity, safety, and toxicity.
I am currently based at Broad Institute of MIT and Harvard where I am advised by Anne Carpenter and Shantanu Singh.
I am also a final year Ph.D. student at the University of Cambridge where I am advised by Andreas Bender

Upcoming and Past Talks

Publications

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Journal of Chemical Information and Modeling, 2024

Integrating Cell Morphology with Gene Expression and Chemical Structure to aid Mitochondrial Toxicity detection

Integrating Cell Morphology with Gene Expression and Chemical Structure to aid Mitochondrial Toxicity detection

Communications Biology, 2022

Comparison of Cellular Morphological descriptors and Molecular Fingerprints for the prediction of Cytotoxicity- and Proliferation-related assays

Comparison of Cellular Morphological descriptors and Molecular Fingerprints for the prediction of Cytotoxicity- and Proliferation-related assays

Chemical Research in Toxicology, 2021

Understanding Biology in the Age of Artificial Intelligence

Understanding Biology in the Age of Artificial Intelligence

arXiv, 2024

Using Chemical and Biological data to Predict Drug Toxicity

Using Chemical and Biological data to Predict Drug Toxicity

SLAS Discovery, 2023

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint models using Similarity to Training data

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint models using Similarity to Training data

Journal of Cheminformatics, 2023

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability

Molecular Biology of the Cell, 2024

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

Journal of Chemical Information and Modeling, 2024

Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in Vivo and in Vitro Data

Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in Vivo and in Vitro Data

bioRxiv, 2024

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

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

bioRxiv, 2024

Calibrated prediction of scarce adverse drug reaction labels with conditional neural processes

Calibrated prediction of scarce adverse drug reaction labels with conditional neural processes

ICLR 2024 DMLR Workshop

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