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.
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.