As a doctoral student with a background in bio- and chemoinformatics, Srijit Seal first became interested in applying machine learning (ML) models to interpret the biological significance of the morphological profiling assay known as Cell Painting while pursuing his master’s degree in the lab of Andreas Bender, Ph.D., at University of Cambridge.
He eventually joined the lab of Cell Painting co-inventor and SLAS Fellow Anne E. Carpenter, Ph.D., at the Broad Institute of MIT and Harvard (Cambridge, MA, USA). His current work in the lab, which became part of his SLAS Ignite presentation at #SLAS2024, is MolToxNet, a versatile and ML-ready benchmark dataset curated from diverse sources, including ChEMBL, PubChem, FDA datasets, and other scientific publications.
"MolToxNet is a two- to three-year, long-term project to develop data sets and then maintain a website," Seal reports. "A few people came up after [the SLAS Ignite presentation]. We exchanged emails and connected with each other via LinkedIn. The SLAS community is very startup and research friendly.”
https://lnkd.in/ezB5jt3H
