NYAGIM is excited to welcome members back for our first in-person event since 2019!

Dr Stephen MacKinnon from Cyclica will present: "Proteome-Scale Drug-Target Interaction Predictions: Approaches & Applications"

The location is JLABS @ NYC (101 6th Ave 3rd floor) & the talk will be followed by a food & drink reception. Please note that we are strictly limited to 70 attendees & we are not able to broadcast this event online so it will be an in-person event only.

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ABSTRACT

Drug Target Interaction (DTI) predictions have recently gained widespread popularity with advances in machine learning & publicly available bioassay datasets, such as BindingDB, DrugBank, PubChem & ChEMBL. Machine learning based strategies frame DTI predictions as a discriminative supervised learning problem, whereby combined pairs of features derived from the ligand (drug) & protein (target) are classified as a binding (positive) or non-binding pair (negative). Several literature-reported DTI models share this overarching classification strategy, but diverge in data representation, feature embedding strategies, training data quality thresholds, scale of underlying datasets, data balance, use of negative training examples, testing protocols & optimization targets. This presentation will review the overall structure of DTI prediction models, including strengths, limitations, & validation pitfalls based on our own first-hand experience developing Cyclica's MatchMaker technology. Moreover, we will discuss the overall role of DTI models fit into today's AI-led drug discovery workflows.

BIO

Stephen MacKinnon is a computational scientist & the Chief Platform Officer at Cyclica. He holds B.Sc. & Ph.D. degrees from the Universities of Waterloo & Toronto respectively, with research emphasis on computational biochemistry & structural biology. Stephen was Cyclica's first staff scientist, where he led the design & implementation of their predictive technologies. Cyclica develops ML- & biophysics- based approaches to model a drug's impacts on greater biological systems & applies these platforms to design & advance molecules that embrace the complexity of disease. In his current role, Stephen plans, coordinates & oversees the ongoing development of all in-house computational platforms to support drug discovery operations.