Estefania Barreto-Ojeda
Estefania Barreto-Ojeda is a computational scientist at Cyclica Inc., where she develops and maintains machine learning pipelines for drug discovery. A physicist by training, she has a PhD in Biophysical Chemistry from the University of Calgary where she developed open source tools to analyze MD simulations. Estefania is an occasional open-source contributor, full time data visualization fan, and seasonal bicycle lover.
Sessions
Development of Machine Learning (ML) pipelines in drug discovery faces different challenges from those in traditional software development. In addition to unique challenges during the data engineering stage, drug discovery pipelines require not only the standard Git tracking for source code but also make versioning of data and ML models necessary. In this talk, we will discuss some of the main challenges when working with biological data and how Data Version Control (DVC) tools help to facilitate data- and model-tracking during the development of ML drug discovery pipelines.