Cambridge Healthtech Institute’s Inaugural
AI for Drug Discovery and Development
Accelerating Drug Discovery- One Use Case At a Time
June 2-4, 2020
Artificial Intelligence (AI), especially deep learning and machine learning, is coming out as disruptive technology for the faster discovery and development of innovative therapies. There is a lot of excitement about the opportunities associated with
the application of AI, but at the same time, a gap exists in understanding these possibilities and applying them to drug discovery and development processes. CHI’s inaugural AI for Drug Discovery and Development conference will address the key questions such as: What can AI and ML do and not do for the pharmaceutical industry? What should be done to harness value out of AI for drug discovery? What measures should be taken to invest and apply AI at various stages of drug development, such as drug design, optimization safety prediction, CMC, quality control, clinical trials, repurposing, and business strategies? What should be the expectation of returns?
Achieving Digital Disruption in Pharma through Artificial Intelligence – Status & Opportunities
Amol Jadhav, PhD, Industry Consultant, Transformational Health, Frost & Sullivan
Building a Small Company to Apply Machine Learning for Rare and Neglected Disease Drug Discovery
Sean Ekins, PhD, DSc, CEO, Collaborations Pharmaceuticals, Inc.
Application of DL Approaches for Non-Target-Based Drug Repurposing
Arash Keshavarzi Arshadi, MS, Research Fellow, College of Medicine, University of Central Florida
Using AI Tools to Accelerate Drug Discovery
Cornelis E.C.A. Hop, Vice President, Drug Metabolism & Pharmacokinetics, Genentech
Active Learning in Lead Optimization
Clayton Springer, PhD, Computational Chemist, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Inc.
A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes, PhD, Banting Fellow, Collins Lab, Broad Institute of MIT & Harvard
Human Genetics Based Drug Discovery- Challenges and Opportunities
Narender R. Gavva PhD, Director, Early Target Discovery, Takeda California, Inc.
Artificial Intelligence Approach to Ligand and Structure-Based Design
Istvan J. Enyedy, PhD, Principal Scientist, Medicinal Chemistry, Biogen
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
ML and AI on ADME/Tox Accelerating Drug Discovery
Barun Bhhatarai, PhD, Investigator, Novartis Institute for Biomedical Research
AI for Acceleration of Drug Development
Bino John, PhD, Associate Director, Data Science, Clinical Pharmacology and Safety Sciences - Data Science and AI, AstraZeneca
AI and ML Approaches to Healthcare Data Integration and Analysis
Shruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine
Toward a Universal Biomedical Data Translator: From Vision to the Working Prototype
Marcin von Grotthuss, PhD, Senior Computational Scientist, Broad Institute of Massachusetts Institute of Technology and Harvard
Segmentation and Classification of Crystalline Structures from 3D X-Ray Microscopy Images in Pharmaceutical Tablets
Pradeep Babburi, MS, Data Scientist, R&D, AbbVie, Inc.
Applications of Artificial Intelligence in Drug Discovery – Separating Hype from Utility
Patrick Walters, PhD, Senior Vice President, Computation, Relay Therapeutics