MIT-Takeda: AI + ML for Target Identification through Pre-clinical Research

MIT Industrial Liaison Program

A four-part series on ML and AI for Drug Development, highlighting  MIT-Takeda joint projects, MIT faculty, Takeda solutions, and startups.

1 Main St

Agenda:9:30 -9:40am: Welcome and Introduction 9:40-10:10am: MIT-Takeda Project D835: Dr. Xiao Tan on “Discovering functional changes in the human gut microbiome using continuous protein representations”10:10-10:40am: Dr. Rocío Mercado on &quot;Deep molecular generative models for drug discovery and optimization&quot;10:40 -11:10am: Clinton Wang on &quot;Identifying radiological biomarkers with generative models&quot;11:10 – 11:25am: Kate Rosner of METiS on “Organ targeted LNPs developed using artificial intelligence”11:25 – 11:40am: Dr. Han Lim of DeepCure on &quot;AI-driven discovery to create better molecules and faster cures for every disease-relevant protein target&quot; (Virtual)11:40 – 11:45am : Concluding remarks11:45 – 12:30pm: Networking lunchSpeaker Bios:Xiao Tan, MD, PhD: Dr. Tan joined Professor Jim Collins’ lab during his Gastroenterology fellowship at Massachusetts General Hospital, where he currently maintains his clinical practice. He joined the Wyss Institute as a Clinical Fellow in the Living Cellular Devices team in 2017. His research focuses on using synthetic biology and microbial engineering to develop new technologies to diagnose and treat GI illnesses.Rocío Mercado, PhD: Dr. Mercado is a postdoc in the Coley group at MIT. Previously, she was a postdoc in the Molecular AI team at AstraZeneca, where she worked on the development of deep generative models for small molecule drug discovery. Prior to this, she completed her PhD in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. Next year, Dr. Mercado will be joining the Data Science and AI faculty at Chalmers University of Technology.Clinton Wang: Clinton Wang is a PhD candidate at MIT CSAIL advised by Polina Golland. His research focuses on medical vision, particularly interpretable and robust deep learning models in the context of neuroimaging and fetal MRI. Previously, Clinton studied biomedical engineering at Yale and worked in the Yale Radiology Research Lab, building interpretable neural networks for liver cancer diagnosis.Kate Rosner: Kate Rosner works in Business Development and Strategy for METiS. METiS combines AI data-driven algorithms, mechanism-driven quantum mechanics and molecular dynamics simulations to calculate API properties, elucidate API-target and API-excipient interactions, and predict chemical, physical and pharmacokinetic properties of small molecule and nucleic acid therapeutics in specific microenvironments.Han Lim, MD, PhD: Dr. Lim is Chief Business Officer at DeepCure, Inc., a biotechnology company based in Boston with a mission to transform preclinical drug discovery with a molecular foundry powered by a quintillion member small molecule library, an automated laboratory for assays, and artificial intelligence (AI).

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MIT-Takeda: AI + ML for Target Identification through Pre-clinical Research

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