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?

AI IN R&D STRATEGY AND BUSINESS DECISION

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

AI IN DRUG DESIGN AND DRUG DISCOVERY- CHALLENGES AND OPPORTUNITIES

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

AI IN SAFETY AND TOXICOLOGY

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 IN TRANSLATIONAL RESEARCH AND DEVELOPMENT

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.

CHALLENGES IN ADOPTION AND IMPLEMENTATION HYPE, TRUST, PRIVACY, EXPLAINABLE AI

Applications of Artificial Intelligence in Drug Discovery Separating Hype from Utility
Patrick Walters, PhD, Senior Vice President, Computation, Relay Therapeutics