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?
Final Agenda
Day 1 | Day 2 | Day 3 | Download Brochure
SC6: An ML/AI Tutorial: From Basics to Advanced - Detailed Agenda
*Separate registration required.
Tuesday, June 2
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. Day 1 of the conference will discuss the challenges and opportunities in applying AI to drug discovery processes, drug design, virtual screening, and in silico prediction of therapeutic targets using several use cases.
10:00 am Main Conference Registration Open
11:15 Chairperson’s Remarks
Amol Jadhav, PhD, Industry Consultant, Transformational Health, Frost & Sullivan
11:25 KEYNOTE PRESENTATION: Using AI Tools to Accelerate Drug Discovery
Cornelis Hop, Vice President, Drug Metabolism & Pharmacokinetics, Genentech
This presentation will delve into the use of Machine Learning- and Artificial Intelligence-based applications in discovery and development projects. A sampling of what will be discussed: a retrospective analysis on predicting potency in drug discovery,
use case data from current and past ADMET projects, and external collaborations to establish the benefits of these approaches.
11:55 Human Genetics-Based Drug Discovery: Challenges and Opportunities
Narender
Gavva, PhD, Director, Early Target Discovery, Takeda California, Inc.
The drug discovery industry adapted patient genetics target identification and validation (TIDVAL) approaches last decade to increase success rates in the clinic. There remain many challenges for human genetics TIDVAL in finding large effect size targets
that can prevent or reverse disease progression. The presentation will cover opportunities for longitudinal studies that couple AI for drug discovery.
12:25 pm Artificial Intelligence Approach to Ligand and Structure-Based Design
Istvan Enyedy, PhD, Principal Scientist, Medicinal Chemistry, Biogen
Ligand and structure-based methods in combination with machine learning models are necessary components of a drug discovery campaign. We can increase the efficiency of optimizing compounds by combining these methods into a multiparameter optimization
platform that combines all three approaches. Preliminary results of this approach will be presented.
12:55 Transition to Lunch
1:00 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own
1:30 Session Break
2:00 Chairperson’s Remarks
Narender Gavva, PhD, Director, Early Target Discovery, Takeda California, Inc.
2:05 AI and the Cloud: Novel Ways to Accelerate InnovatioN
Todd Neuville, Leader, Worldwide Business Development, LeaderLife Sciences, Amazon Web Services (AWS)
Learn how pharma companies are working with artificial intelligence and machine learning (AI/ML) to accelerate research, enhance their clinical trials, improve manufacturing, and better understand real-world data. Hear how cloud technology is helping
to expand the use of AI along the life sciences value chain to accelerate time to market for new products and increase operational efficiency.
2:35 A Deep Learning Approach to Antibiotic Discovery
Jonathan Stokes, PhD, Banting Fellow, Collins Lab, Broad Institute of MIT & Harvard
To address the antibiotic-resistance crisis, we trained a deep neural network to predict new antibiotics. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub – halicin – that is structurally
divergent from conventional antibiotics and displays activity against a wide spectrum of pathogens. Halicin also effectively treated Clostridioides difficile and Acinetobacter baumannii infections in mice. Deep learning approaches have utility in
expanding our antibiotic arsenal.
3:05 Fringing – Or, How to Best Search for Gold Nuggets
Clayton Springer, PhD, Computational Chemist, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Inc.
The Fringing approach is inspired by Kriging. Kriging is a method from geostatistics which estimates the most likely distribution of gold based on samples from a few boreholes. Fringing translates this approach to chemical space and allows algorithmic
exploitation and exploration of the chemical space.
3:35 Sponsored Presentation (Opportunity Available)
4:05 Networking Refreshment Break and Transition to Keynote
4:25 - 6:05 Driving Entrepreneurial Innovation to Accelerate Therapeutic Discoveries
The life sciences community has an unprecedented scientific arsenal to discovery, develop and implement treatments, cures and preventions that enhance human healthcare.
Moderator: Nadeem Sarwar, President, Eisai Center for Genetics Guided Dementia Discovery (G2D2), Eisai Inc.
Panelists: Anthony Philippakis, Chief Data Officer, Broad Institute; Venture Partner, GV
Barbara Sosnowski, Vice President and Global Head, Emerging Science & Innovation Leads, WWRDM, Pfizer
John Hallinan, Chief Business Officer, Massachusetts Biotechnology Council
6:05 Welcome Reception in the Exhibit Hall with Poster Viewing
7:10 Close of Day
Day 1 | Day 2 | Day 3 | Download Brochure
Wednesday, June 3
On Day 2 of the conference, we will take a deep dive into the preclinical, translational and clinical topics. The conference will examine the role of AI in making sense of clinical data, predicting clinical trial outcomes, finding correct patients for clinical trials, analyzing real-world evidence, making sense of complex medical data, and data integrity. The conference will also explore opportunities through use cases in the preclinical stage such as preformulation studies as well as safety and tox evaluation.
7:30 am Registration Open and Morning Coffee
8:10 Chairperson’s Remarks
Janaki Iyer, Team Lead/Senior Medical Writer, INVIVO Communications, Inc..
8:15 KEYNOTE PRESENTATION: AI for Acceleration of Drug Development
Bino John, PhD, Associate Director, Data Science, Clinical Pharmacology and Safety Sciences – Data Science and AI, AstraZeneca
Drug development is an expensive and costly endeavor, costing an average of 2.6 billion dollars to bring a drug to market. Artificial Intelligence is essential in reducing the costs and time to bring these to the clinic. This talk will highlight some
of the current AI initiatives at AstraZeneca, spanning chemical and biological data use cases that seek to improve drug design and develop safer medicines.
8:45 AI and ML Approaches to Healthcare Data Integration and Analysis
Shruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine
With the increase in availability of clinical trial data, AI and Machine Learning Approaches are becoming imperative in mining and finding clinically significant insights. In this talk, I will provide an overview of the various approaches currently used
to tackle the big-data problem in pharma.
9:15 Boosting Clinical Trial Success Rates with AI Strategies
Janaki Iyer, Team Lead/Senior Medical Writer, INVIVO Communications, Inc.
The typical drug discovery and development process, commonly termed as “bench to bedside”, lasts about 10-15 years and costs over a billion dollars. Failed clinical trials can lead to tremendous losses in terms of both time and money. This
talk will discuss AI strategies as a viable option to enhance trial designs, improve patient recruitment strategies, and advance patient monitoring with the aim of maximizing overall clinical trial success rates.
9:45 Sponsored Presentation (Opportunity Available)
10:15 Coffee Break in the Exhibit Hall with Poster Viewing
11:00 Strategies for Building AI-Ready Data Sources and (Semi)Autonomous Reasoning Agents Operating on Top of Them
Marcin von Grotthuss, PhD, Senior Computational Scientist, Broad Institute of Massachusetts Institute of Technology and Harvard
Here, we present a prototype Translator framework and architecture, which we have developed for integrating semantically, annotated Knowledge Sources (over 40) and for creating a data platform to support automated reasoning and serendipitous discovery
of new ‘facts’ or interesting and testable hypotheses. We also discuss the strategies of how to integrate and provide high-value AI-ready data sources as well as how to develop (semi) autonomous reasoning agents that would advance reasoning
through innovative uses of these knowledge sources.
11:30 Segmentation and Classification of Crystalline Structures from 3D X-Ray Microscopy Images in Pharmaceutical Tablets
Pradeep Babburi, MS, Data Scientist, R&D, AbbVie, Inc.
Here we present ongoing work using image analysis and machine/deep learning techniques to segment and differentiate the crystalline and amorphous phases of the drug as well as other crystalline substances like silicon dioxide (SiO2) from 3D x-ray microscopy
(XRM) scans. Our work demonstrates the results of basic image analyses, geometric feature extraction, as well as unsupervised and supervised learning models trained to identify the crystalline structures based on their morphology.
12:00 pm Sponsored Presentation (Opportunity Available)
12:30 Transition to Lunch
12:35 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own
1:05 Session Break
1:45 - 3:15
Lgr5 Stem Cell-Based Organoids in Human Disease
Hans Clevers, MD, PhD,
Principal Investigator of Hubrecht Institute and Princess Máxima Center, CSO of HUB Organoids Technology
Organoid technology opens a range of applications in fields such as physiology, study of disease, drug development and personalized medicine. Human organoids represent excellent disease models, be it infectious, hereditary or malignant Eventually,
cultured mini-organs may be used to replace transplant organs from donors. I will describe how we originally created ‘mini-guts’ via 3D culture systems of stem cells of the small intestine and colon, and then expanded the technology to
virtually all human organs.
Systematically Drugging Ras
Stephen Fesik, PhD,
Professor of Biochemistry, Pharmacology, and Chemistry, Orrin H. Ingram II Chair in Cancer Research, Vanderbilt University School of Medicine
K-Ras is a small GTPase that is mutated in pancreatic (90%), colon (50%), and lung (30%) carcinomas. Downregulation of activated Ras reverses the transformed phenotype of cells and results in the dramatic regression of tumors in murine xenograft models.
Thus, K-Ras inhibition represents an attractive therapeutic strategy for many cancers. In this presentation, I will discuss our efforts to directly target Ras at two sites and target SOS, a molecular partner of Ras, with activators and inhibitors.
3:15 Refreshment Break in the Exhibit Hall with Poster Viewing
4:00 Chairperson’s Remarks
Barun Bhhatarai, PhD, Investigator, Novartis Institute for Biomedical Research
4:05 ML and AI on ADME/Tox Accelerating Drug Discovery
Barun Bhhatarai, PhD, Investigator, Novartis Institute for Biomedical Research
ML- and AI-related approaches have been tested and applied in various areas within Novartis. In ADMETox, ML approaches are serving intended purposes and complementing experimental methods. With the advent of AI, ingenious deep learning algorithms,
and powerful micro-processors, we have explored its anticipated benefit in preclinical and clinical programs. Our various efforts on data digitization, ML and AI implementation, and collaborations will be discussed with specific examples from
ADMETox.
4:35 Artificial Intelligence and Small-Molecule Drug Metabolism
Joshua
Swamidass, MD, PhD, Assistant Professor, Immunology and Pathology, Laboratory and Genomic Medicine; Faculty Lead, Translational Informatics, Institute for Informatics, Washington University
We have been building artificial intelligence (AI) models of metabolism and reactivity. Metabolism can both render toxic molecules safe and safe molecules toxic. The AI models we use quantitatively summarize the knowledge from thousands of published
studies. The hope is that we could more accurately model the properties of medicines to determine whether metabolism renders drugs toxic or safe. This is one of many places where artificial intelligence could give traction on the difficult questions
facing the industry.
5:05 Find Your Table, Meet Your Moderator
5:10 Roundtable Breakout Discussions - View Details
TABLE: Decoding AI: Making the Case for Artificial Intelligence in the Pharma Industry
Moderator: Amol Jadhav, PhD, Industry Consultant, Transformational Health, Frost & Sullivan
TABLE: Machine Learning in Action: Moving Beyond Hype
Moderator: Sean Ekins, PhD, DSc, CEO, Collaborations Pharmaceuticals, Inc.
5:45 Reception in the Exhibit Hall with Poster Viewing
6:45 Close of Day
Day 1 | Day 2 | Day 3 | Download Brochure
Thursday, June 4
There is a lot of excitement about the opportunities associated with AI, but at the same time, a gap exists in understanding these possibilities. Day 3 gives strategic talks from a business perspective, allowing you to assess the value of investing in AI to supercharge pharma R&D. Also, we discuss the challenges in the adoption and implementation of AI in pharma. We discuss in detail through interactive talks, panels, and discussion issues such as separating the hype from reality, trust, privacy, explainable AI and more.
8:00 am Registration Open and Morning Coffee
8:30 - 9:40 Applications of Artificial Intelligence in Drug Discovery – Separating Hype from Utility
Patrick Walters, PhD, Senior Vice President, Computation, Relay Therapeutics
Over the last few years, there has been tremendous interest in the application of artificial intelligence and machine learning in drug discovery. Ultimately, the success of any predictive model comes down to three factors: data, representation,
and algorithms. This presentation will provide an overview of these factors and how they are critical to the successful implementation and deployment of AI methods.
9:40 Coffee Break in the Exhibit Hall with Poster Viewing
10:25 Chairperson’s Remarks
Sean Ekins, PhD, DSc, CEO, Collaborations Pharmaceuticals, Inc.
10:30 PANEL DISCUSSION: Challenges in Adoption and Implementation: Hype, Trust, Privacy, and Explainable AI
Moderator:
Sean Ekins, PhD, DSc, CEO, Collaborations Pharmaceuticals, Inc.
Panelists:
Joseph Lehar, PhD, Adjunct Professor, Bioengineering and Bioinformatics, Boston University
Patrick Walters, PhD, Senior Vice President, Computation, Relay Therapeutics
Amol
Jadhav, PhD, Industry Consultant, Transformational Health, Frost & Sullivan
Jonathan Lefman, PhD, Developer Relations Manager, Healthcare and Life Sciences, Nvidia
Bino John,
PhD, Associate Director, Data Science, Clinical Pharmacology and Safety Sciences – Data Science and AI, AstraZeneca
Narender Gavva, PhD, Director, Early Target Discovery, Takeda California, Inc.
11:30 Achieving Digital Disruption in Pharma through Artificial Intelligence – Status & Opportunities
Amol
Jadhav, PhD, Industry Consultant, Transformational Health, Frost & Sullivan
This presentation will focus around the commercial aspects and call out major current application areas of AI in the pharmaceutical industry. Opportunity assessment within specific sub-segments, themes driving adoption, preview of successful business
models and relevant case studies, expectation on returns, and global scenarios will be discussed.
12:00 pm Sponsored Presentation (Opportunity Available)
12:30 Transition to Lunch
12:35 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own
1:05 Dessert and Coffee Break in the Exhibit Hall with Poster Viewing
2:00 Chairperson’s Remarks
Jonathan Lefman, PhD, Developer Relations Manager, Healthcare and Life Sciences, Nvidia
2:05 Application of AI in Pharma R&D: Use Cases
Jonathan Lefman, PhD, Developer Relations Manager, Healthcare and Life Sciences, Nvidia
2:35 Building a Small Company to Apply Machine Learning for Rare and Neglected Disease Drug Discovery
Sean
Ekins, PhD, DSc, CEO, Collaborations Pharmaceuticals, Inc.
Collaborations Pharmaceuticals, Inc. (CPI) aims to streamline the development of drugs for rare and neglected tropical diseases. Using our machine learning technology and combining forces with many academic collaborators, we have identified treatments
for parasites (T. cruzi), bacteria (M. tuberculosis), and viruses (Ebola, HIV, etc.), progressing to in vivo models. I will describe how we can also apply this approach for rare diseases.
3:05 Application of DL Approaches for Non-Target-Based Drug Repurposing
Arash Keshavarzi Arshadi, MS, Research Fellow, College of Medicine, University of Central Florida
We talk about the use of DL approaches – especially transfer learning – for predicting the potency of already approved drugs for other diseases. Since the target of the known drugs would be completely different types of biomolecules
in different cells, pursuing drug repositioning with target-based approaches would not be applicable. Also, many target molecules or mechanisms of their interactions are not discovered yet. Therefore, non-target approaches would be suitable
for this manner. In the case of having low data, we will discuss how transfer learning would increase accuracy and recall.
3:35 Close of Conference
Day 1 | Day 2 | Day 3 | Download Brochure