Cambridge Healthtech Institute’s 2nd Annual

Artificial Intelligence for Early Drug Discovery

AI & Machine Learning for Drug Design and Lead Optimization

April 15-16, 2020



The Artificial Intelligence for Early Drug Discovery conference will bring together a diverse group of experts from bioinformatics, chemistry, target discovery, DMPK, and toxicology to talk about the increasing use of computational tools, artificial intelligence (AI) models, machine learning (ML) algorithms, and data mining in preclinical drug development. Starting with an overview of current challenges and opportunities, the talks will highlight how AI/ML can help with drug design, target identification, lead optimization, PK/PD predictions, and early safety assessments. The speakers will offer insights into the caveats and limitations of AI/ML-based decision-making using relevant case studies and research findings. The conference will offer an excellent opportunity to network and share ideas and best practices.


Preliminary Agenda

UNDERSTANDING CAVEATS OF USING AI/ML PREDICTIONS

FEATURED PRESENTATION: Artificial Intelligence and Small-Molecule Drug Metabolism

S. Joshua Swamidass, MD, PhD, Assistant Professor, Immunology and Pathology, Laboratory and Genomic Medicine; Faculty Lead, Translational Informatics, Institute for Informatics, Washington University

Talk Title to be Announced

Kuan-Fu Ding, MSc, PhD, CSO and CTO, Cubismi, Inc.

AI FOR DRUG DESIGN, COMPOUND SCREENING AND PRIORITIZATION

FEATURED PRESENTATION: Benefits, Limitations and Diversity of AI Models in Drug and Target Discovery

Ruben Abagyan, PhD, Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego

Applied Machine Learning in Compound Mechanism Deconvolution

Yuan Wang, PhD, Senior Principal Scientist, Head of TPD Data Science, UCB Pharma

Deep Generative Autopilot for the Real-World Design of Novel Lead Compounds

Sang Ok Song, PhD, Co-Founder and Chief Transformation Officer, Standigm, Inc.

USE OF AI/ML TO PREDICT ADME & DRUG SAFETY

Integration of Structure-Based and Machine Learning Approaches to Predict Inhibition of Drug Metabolizing Enzymes

Maria A. Miteva, PhD, Research Director, Molécules Thérapeutiques in silico (MTi), Inserm Institute

Understanding the Applicability and Limitations of in silico and in vitro Safety Models towards the Design and Selection of the Safest Drug Candidates

Takafumi Takai, PhD, Senior Scientist, Discovery Toxicology, Drug Safety Research & Evaluation, Takeda, Inc.

PREDICTING DDI AND DRUG-TARGET INTERACTIONS

Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions

Arvind Rao, PhD, Associate Professor, Department of Computational Medicine and Bioinformatics, University of Michigan

YOUNG INVESTIGATOR PRESENTATIONS:
Applications of Deep Learning-Based Approaches for Non-Target-Based Drug Discovery

Arash Keshavarzi Arshadi, MS, Doctoral Student/Research Fellow, College of Medicine, University of Central Florida

Drug Hybridization Approach to Drug Design

Clinton Egbe, PharmD, Master’s Student in Drug Design and Discovery, University of Salford

* The program is subject to change without notice, due to unforeseen reason.

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Update History
2019/11/25
Sponsor updated
2019/10/31
Agenda,Sponsor updated