Augmented Intelligence in Drug Discovery Xchange
East Coast 2021
May 18 & 20

Welcome to hubXchange’s East Coast Augmented Intelligence (AI) in Drug Discovery Xchange 2021, bringing together executives from pharma and biotech to address and find solutions to the key issues faced in AI-led drug discovery.
Discussion topics will cover Data Quality, Target Identification, Lead Generation, Lead Optimization and Drug Response Prediction.
Take advantage of this unique highly interactive meeting format designed for maximum engagement, collaboration and networking with your peers.

Data Quality

Time

Titles and Bullets

Facilitator

DAY 1 (May 18, 2021)

8:30 – 9:00am

Opening Address & Keynote:

Augmented Intelligence; A More Realistic Paradigm in Data Driven Drug Development?

Augmented Intelligence: emerging realism after the hype?
Eroom’s Law: the ever pressing need for data driven drug development
Data quality and machine readability: the elephant in the AI room?
Collaborative Intelligence: evolving towards ever closer human-machine interaction

Arie Baak

Co Founder
Euretos

Arie Baak is one of the co-founders of Euretos (est. 2012), an AI product & services company that enables data driven disease and drug research. Before moving into life sciences, Arie worked in various strategic innovation roles in the mobile telecoms and internet infrastructure markets. In these high performance/data volume environments he got deeply involved in generating actionable insight to end users long before the term ‘big data analytics’ became fashionable. For the past 10 years, Arie has been applying this expertise to enabling data driven disease and drug research working with many of the world’s leading pharma, biotech and academic institutions.

9:10 – 10:10am

Defining Data Quality and How It Can be Achieved and Maintained at Scale

How do we assess and test the quality of a dataset? What threshold does it need to hit in order to be included in training an AI/ML model?
How do we verify possible dataset challenges (e.g. outliers) at scale? Is it even possible beyond a handful of data points?
What do you think about the peer review process to be brought down to the data world? Is there is a parallel in that world, and if not can there be one? Should we be adopting dataset standards that already exist?

Norman Azoulay

Director, Scientific Products
Excelra

Norman comes with a rich product management background that includes experience in developing and launching enterprise solutions. He brings a user centric and analytically driven approach; the ability to identify areas of growth and opportunity; and experience in setting strategic goals that align with the business objectives of the organization. Norman is leading Excelra’s rapid foray into technology solutions to accelerate innovation in drug development. He holds a Master of Liberal Arts, Extension Studies with a focus in Biology, from Harvard University.

Noor Shaker

Founder and Chief Executive Officer
Glamorous AI

Noor is a serial biotech entrepreneur with a track record of achievements in AI having held an Assistant Professorship from Aalborg University. Noor has published numerous papers in the field of artificial intelligence and its application to drug discovery and is an inventor on a handful of patents. She is passionate about data and AI and on a mission to cure disease with the power of human and machine learning. She is currently the CEO at GlamorousAI, a biotech company that pushes the boundaries to what is possible with AI to cure debilitating diseases. She is a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women for 2019.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

1-2-1 Meetings

1:40 – 2:40pm

Where is High-Quality Data Located and How Can They Be Best Utilized?

Explore the effective, and realistic approaches to annotate and share data; define data quality aspects for ML applications
How do we best utilize and interpret data of quality?
Discuss why executing data quality is a continuous process
Share experiences in tackling data quality challenges

Bryce Allen

Co-Founder & CEO
Differentiated Therapeutics

Bryce Allen is the Head of Integrated Data Sciences, leading the data analytics and machine learning team. Bryce has contributed significantly to the company in several key areas, including conformational genetics, free energy calculations, uncertainty quantification, de novo design, and database infrastructure. Before joining SiTX, he was a Postdoctoral Fellow in Biomedical Informatics at Harvard Medical School, where he worked on clinical trajectory modeling in Inflammatory Bowel Disease. Additionally, during his PhD training, Bryce worked as a Biomedical Data Scientist at the Library of Integrated Network-based Cellular Signatures (LINCS) Data Coordination and Integration Center and helped lead the architecture and development of data storage, query, and analysis infrastructure. He also developed custom ETL processes and machine-learning models for chemical and genomic entities used in the LINCS Consortium.

DAY 2 (May 20, 2021)

8:30 – 9:00am

Opening Address & Keynote Presentation:

Using Causal Data to Empower Drug Discovery

Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery

Daniel Jamieson

CEO and Founder
Biorelate

Daniel is the CEO and founder of Biorelate. Daniel founded Biorelate during a PhD in computational biology at the University of Manchester, after supporting the successful identification of drug repurposing opportunities with Pfizer. Daniel’s focus remains on growing Biorelate into a world leading enterprise, helping pioneering companies in their mission to develop life-saving innovations.

9:10 – 10:10am

Effective Approaches in Multi-Omics Data Integration for ML Application

Share experiences in data curation
What are the best methods in facilitating data integration for ML application?
Discuss how data bias can be lessened via parallel analysis of omics data and how can we achieve this?
Explore the most efficient application integrated data for drug discovery

Chi Zhang

Senior Scientist, Bioinformatics
Eisai Center for Genetics Guided Dementia Discovery (G2D2)

Dr. Zhang have a Ph.D in Computational Biology from Washington University in St. Louis. He is experienced in multi-omics data integration and is currently the data science lead at Eisai G2D2 research unit.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

Poster Session- Actoris

Combining AI with Robotics to Accelerate Project Progression from Idea to IND

Drug discovery comes down to making a series of critical go/no-go decisions – which target to pursue, which molecule to select for further testing, etc. The main bottleneck in conventional and in AI-augmented drug discovery is access to reproducible, well annotated, fully structured wet lab data that powers these decisions
Explore how we combine our partners’ AI with our robotic platform in Oxford, employing end-to-end automation of entire drug discovery processes to generate and capture 100x more datapoints per assay for better and faster decision-making
In addition to a look under the hood of the unique technology platform developed by Arctoris, the session will also highlight success stories from biotech and pharma companies (including several of the world’s leading AI drug discovery companies) in the US, Europe and Asia-Pacific who already work with Arctoris to accelerate their work

Actoris

1:40 – 2:40pm

How Can We Achieve Meaningful Pre-Competitive Data Cross-Sharing?

Discuss how to balance data obfuscation with data utility for meaningful data sharing
Explore if organisations are over-valuing their data assets and preventing meaningful data sharing
Explore the ideal composition of data-sharing consortiums and initiatives- larger pharma and biotech versus smaller start-ups

Nadeem Sarwar

Founder & President
Eisai Center for Genetics Guided Dementia Discovery (G2D2)

2:40 – 2:45pm

Closing Address

Target Identification

Time

Titles and Bullets

Facilitator

DAY 1 (May 18, 2021)

8:30 – 9:00am

Opening Address & Keynote:

Augmented Intelligence; A More Realistic Paradigm in Data Driven Drug Development?

Augmented Intelligence: emerging realism after the hype?
Eroom’s Law: the ever pressing need for data driven drug development
Data quality and machine readability: the elephant in the AI room?
Collaborative Intelligence: evolving towards ever closer human-machine interaction

Arie Baak

Co Founder
Euretos

9:10 – 10:10am

What Makes a Good Target?

What data are we missing for in silico target identification?
What has changed in the last few years to enable us to go after previously unidentified targets?
Should we be looking at targets as groups rather than individual protein targets?

Mark Hughes

Head of Business Development
Biorelate

Mark is a strong professional with a Doctor of Philosophy (PhD) focused in Genetics from University of Liverpool. Experienced Director with a demonstrated history of working in the biotechnology industry. With over 20 years experience in Product Management and Business Development. Over the 20+ years I have worked with start ups like Silicon Genetics, GeneGo and Biorelate through to larger organisations like Thomson Reuters and WuXi Nextcode. Working from new product innovation and release through to developing the full sales cycle has given me insights into the latest technologies and issues that need addressing in drug discovery. Skilled in Sequence Analysis, DNA Sequencing, Bioinformatics, Transcriptomics, ML/AI and Enterprise Software in relation to drug discovery. Having worked with curated databases over the years and understanding their limitations the Biorelate technology automatically curates the biomedical literature making the latest findings available for advancing drug discovery using the very latest AI/NLP technology.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

1-2-1 Meetings

1:40 – 2:40pm

Transforming Public Domain Literature Data Oceans into Actionable Knowledge

How should public data be manipulated to expedite drug target selection; identification of patient demographics for clinical trials; and spotting of trends and novel technologies
What public domain sources should we access and what are the right sources to extract
Extracting the information component- what to parse and what tools are available?
How to transform extracted information into actionable knowledge
Discuss the challenges of bias; balancing novelty vs. well-established facts; and sparse data and over-generalization

Peter Henstock

Machine Learning & AI Technical Lead
Pfizer

Peter Henstock is the Machine Learning & AI Lead at Pfizer Inc. in the Quantitative Data Sciences group. His work has focused at the intersection of visualization, AI, statistics and software engineering applied mostly to drug discovery but more recently to clinical trials. He has developed over 20 novel visualization tools for screening assays, microarray, genomic analysis, and patent mining. Peter holds a PhD in Artificial Intelligence from Purdue University and 6 Master’s degrees. He teaches graduate AI, Software Engineering, and Computer Vision courses at Harvard.

DAY 2 (May 20, 2021)

8:30 – 9:00am

Opening Address & Keynote Presentation:

Using Causal Data to Empower Drug Discovery

Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery

Daniel Jamieson

CEO and Founder
Biorelate

9:10 – 10:10am

AI/ML Technologies for Disease Pathway Modelling

How accurate are current models for disease pathways- can we confidently predict the outcomes of pathway perturbation?
Discuss how an understanding of disease biology will serve as the main component to AI- allowing the model to highlight drug targets for R&D focus
Explore the communications gaps between domain experts and data scientists to ensure that data collection and AI model selection are fit-for-purpose
Discuss the current landscape of knowledge graph application in pathway modelling

Paul Nioi

Vice President, Discovery and Translational Research
Alnylam Pharmaceuticals

Paul Nioi joined Alnylam in March 2018 and leads both the human genetics and translational research functions. Building upon his depth of prior experience, he has led the creation of the Alnylam Translational Genetics Center (ATGC), putting in place a capability that allows the deep mining of genotype-phenotype databases representing hundreds of thousands of individuals. Paul’s team apply these data to identify new drug targets and to gain important insights into disease trajectories to aid patient discovery efforts. His group is also responsible for advancing new liver and CNS RNAi therapeutics for a variety of diseases.

Prior to Alnylam, Paul spent 15 years in the biotech and pharmaceutical industry including tenures at deCODE genetics, Amgen and Schering-Plough.

Paul obtained his academic training at the University of Edinburgh (BSc) and the University of Dundee (PhD).

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35 – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

GERO Poster Session

How AI helps to utilize Genetic, Transcriptomic And Clinical Data for Target Identification in Complex Diseases?

Disease target identification is among the most challenging issues in biomedicine
The sheer size of the genetic signal poses a nearly insurmountable challenge in terms of the number of samples required for meaningful association studies
We demonstrate the application of a state-of-the-art deep neural network for feature selection (dimensionality reduction) and non-biological variance (batch) removal in transcriptomic signals
We showcase the integration of vast experimental data involving gene expression variation in response to genetic knockdowns, ultra-rare damaging mutations from whole-exome sequences, and large clinical data from large human cohorts to prioritize molecular targets associated with a diseasePo

Peter Fedichev

CEO & Co-Founder
GERO

Ph.D. from the University of Amsterdam. Co-founder of Gero, a data-driven biotech company. GERO.AI is a platform that utilizes Whole-Exome Sequencing data for in-human AI Drug Discovery, while GeroSense is Gero’s arm creating Digital Biomarkers. An author of 75+ published papers in multiple domain areas.

1:40 – 2:40pm

Augmented Intelligence in Drug Target Selection and Triage

How can AI/ML be used to develop drug target selection and exclusion criteria?
Explore effective approaches in using AI to expand understanding and build confidence in targets
True artificial intelligence-led target selection and triage with minimal human input- are we there yet?

Morten Sogaard

Vice President and Head, Genome Sciences & Technologies,
Pfizer

Morten Sogaard, VP Target Sciences at Pfizer oversees genetics, functional genomics, computational biology and target validation group as well as “Innovative target exploration networks” to generate first in class targets from emerging science, and he also oversees companion diagnostics at Pfizer. Previously he helped define and implement Pfizer’s R&D data, AI and platform technology investment strategies and external innovation.

Morten received his Ph.D. from Univ of Copenhagen .and did postdoctoral studies with Nobel laureate Jim Rothman at MSKCC. He started his industry career at Pharmacia, where he immune oncology biologics discovery, then moved on to AstraZeneca and Boehringer Ingelheim where he had senior roles leading biotechnology and genomics platform capabilities, and also briefly served as Head of R&D Informatics at BI.

2:40 – 2:45pm

Closing Address

Lead Generation

Time

Titles and Bullets

Facilitator

DAY 1 (May 18, 2021)

8:30 – 9:00am

Opening Address & Keynote:

Augmented Intelligence; A More Realistic Paradigm in Data Driven Drug Development?

Augmented Intelligence: emerging realism after the hype?
Eroom’s Law: the ever pressing need for data driven drug development
Data quality and machine readability: the elephant in the AI room?
Collaborative Intelligence: evolving towards ever closer human-machine interaction

Arie Baak

Co Founder
Euretos

9:10 – 10:10am

Overcoming Hurdles in Drug Discovery with AI: Pitfalls, Progress and Strategies for Success

AI and ML in the early stages of drug discovery
Challenges faced when bringing AI and ML to the traditional mindsets of medicinal chemistry
Benefits and potential pitfalls of exploring the ever-expanding chemical space, like on-demand synthetic libraries scaling beyond billions of molecules
What does the future hold for AI and ML in early development?

Hideyoshi Fuji

Manager, Advanced Informatics and Analytics,
Astellas

Hideyoshi is currently leading AI-driven drug discovery in Astellas Pharma both through internal technology development and collaboration with external partners as Manager of Advanced Informatics and Analytics. With the increasing need for data-driven drug discovery strategy utilizing machine learning and/or AI, he is applying cutting-edge technologies such as deep learning to practical applications, for instance, conducting applied research on efficient molecular design and highly accurate compound property prediction.

Hideyoshi holds the degree of PhD in Pharmaceutical Sciences and the Pharmacist License in Japan, and prior to joining Astellas Pharma in 2009, he was certified as a Super Creator under the MITOU program in 2006 and studied high-performance molecular simulations for drug discovery under the Junior Research Associate program at RIKEN in 2007-2009.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:40 – 2:40pm

Exploring Tactics for AI/ML Application in Developing TPP (Target Product Profile)

Share obfuscated TPPs and the tactics of AI/ML application for developing the TPPs
Explore other AI tools or omics approach from different therapeutic areas to light a new path for your TPP development
What is the ideal combination of human interaction and AI for the development of TPPs?

Minmin Wang

Senior Vice President of Technology and Informatics
Acerand Therapeutics

Minmin is a senior biopharma leader with 20 years of comprehensive achievements in drug discovery, development, corporate strategy and people management. She has led 8 clinical stage cross-functional teams across two therapeutic areas to deliver key milestones from IND to phase 2 readout. Minmin has delivered 10+ clinical candidates, 5 patents, 20+ peer reviewed scientific publications during as bench computational chemist and team leader. She has built a fully functional R&D facility of 100+ scientists and staff with substantial research portfolio deliverables in Shanghai, China within 5 years. 2 molecules reached FHD milestones. Minmin had extensive knowledge and working experiences of predictive modeling, machine learning, and informatics. She founded and chaired of the first international chapter of American Chemical Society in China from 2011 – 2013.

DAY 2 (May 20, 2021)

8:30 – 9:00am

Opening Address & Keynote Presentation:

Using Causal Data to Empower Drug Discovery

Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery

Daniel Jamieson

CEO and Founder
Biorelate

9:10 – 10:10am

Creating Viable Proof of Concept Molecules with AI/ML to Initiate Lead Generation

Discuss how AI can be effectively applied for the development of lead generation models using internal and external data
Explore the current AI capabilities in predicting drug-like properties
How much should you trust your model? How can you mitigate and plan for uncertainties in the model?

Govinda Bhisetti

Head of Computational Chemistry
Biogen

Govinda Bhisetti is a Principal Investigator and Head of Computational Chemistry at Biogen. Previously, he worked at Vertex Pharmaceuticals for 22 years where he led drug design efforts on several drug discovery projects. His research at Vertex led to the discovery of three FDA approved drugs: Agenerase, Lexiva and Incivek. He is a co-inventor of these drugs and named inventor on 26 patents. He has also published 74 research papers including review articles and book chapters. His current activities include application of state of the art computational methods in the discovery of novel drugs for CNS diseases.

Govinda obtained his Ph.D. from Indian Institute of Science, Bangalore, India and worked as a post-doctoral fellow at Scripps Research Institute, La Jolla, CA before joining Vertex.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

Teselagen Poster Session

Accelerating Generation and Optimization of Leads via AI-powered Platform for Drug Discovery

Presentation of a new AI-powered operating system for managing and integrating genotypic and phenotypic data to efficiently generate novel leads and design candidates, significantly reducing R&D costs
Discussion about the importance of AI/ML embeddings for effectively reducing the dimensionality of lead space, and automatically extracting relevant features of leads
Discussion of different generative models used to efficiently generate lead candidates with physicochemical properties of interest
Discussion about the use of sequential model-based optimization techniques for cost-efficiently exploring and generating new leads

Teselagen

1:40 – 2:40pm

Effective AI/ML Application in Assay Development and Leads Screening

How can AI/ML be applied for the development and selection of screening libraries and assays
Explore the best AI approaches to improving virtual screening outcome and Hit ID
Discuss how to validate in silico predictions at scale using in vivo models

Lena Dolgikh

Associate Director, Chemical Data Science
Vertex

2:40 – 2:45pm

Closing Address

Lead Optimization

Time

Titles and Bullets

Facilitator

DAY 1 (May 18, 2021)

8:30 – 9:00am

Opening Address & Keynote:

Augmented Intelligence; A More Realistic Paradigm in Data Driven Drug Development?

Augmented Intelligence: emerging realism after the hype?
Eroom’s Law: the ever pressing need for data driven drug development
Data quality and machine readability: the elephant in the AI room?
Collaborative Intelligence: evolving towards ever closer human-machine interaction

Arie Baak

Co Founder
Euretos

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

1-2-1 Meetings

1:40 – 2:40pm

Accelerating the Optimization of Small Molecules Using AI/ ML-Led Approaches

Explore application of AI in speeding drug-design-test-analysis cycle
What are the current successes and challenges in AI-led small molecules optimization?
How effective are AI at suggesting novel, synthesizable compound design?
Thinking of ADMET at the stage of compound design
Retrosynthesis to suggest more efficient path to make compounds

Huijun Wang

Head of Cheminformatics and AI in Chemistry
Agios Pharmaceuticals

Dr. Huijun Wang is a talented professional with expertise in chemical informatics, chemical biology, machine learning/AI, information retrieval, and database design. While born and raised in China, she flourished her PhD career in US from Indiana University at Bloomington, where she was focused in data integration and data/text mining for large scale knowledge discovery from both chemical and biological perspective. Later on, she was recruited to Pfizer in 2010, and quickly began to lead critical projects implementing data integration/mining approaches to support project decisions. At the same time, she started to develop informatics applications into various therapeutic areas.

In 2104, Huijun joined Merck at NJ. She helped the organization swiftly expand its footprints in informatics applications in the areas of system chemical biology, competitive intelligence, visual screening, and ligand-based compound design. Impressed by the fast hardware and algorithm development, Huijun was not hesitated to pioneer experimenting AI approaches to speed up the drug design cycle.

Huijun joined Agios at 2019 and leading the cheminformatics and AI in drug design. She is very much interested in new technologies that could identify the right targets and designing better molecule fast.

DAY 2 (May 20, 2021)

8:30-9:00am

Opening Address & Keynote Presentation:

Using Causal Data to Empower Drug Discovery

Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery

Daniel Jamieson

CEO and Founder
Biorelate

9:10 – 10:10am

AI/ML Approaches from Biologics Discovery to Clinic; CMC Attributes and Preclinical Translation

How can AI/ML be applied for the optimisation of macromolecules?
Discuss where AI/ML can be applied to streamline macromolecules production
All predictive models have uncertainties- how to you search and account for them to de-risk clinical translation?

Vibha Jawa

Executive Director, Head of Biotherapeutics Bioanalysis NDB Org
Bristol Myers Squibb

10:10 -11:00am

BREAK

11:00 -11:30am

1-2-1 Meetings

11:35am -12:05pm

1-2-1 Meetings

12:05 -1:00pm

BREAK

1:00 – 1:30pm

Poster Session

AI-Assisted Generative Design of Synthetically Feasible Chemical Space to Accelerate Lead Optimization

ML and deep learning models in lead optimization accelerate discovery
Virtual or real optimization cycles require rapid lead analog design or scaffold hopping ideation for exploitation and exploration
Generative design by deep networks and simple focused library enumeration do not consider synthetic feasibility therefore laboratory success rates and cycle timelines often suffer
A new comprehensive generative concept using AI-assisted in silico synthesis can effectively exploit and explore relevant chemical space quickly and drive time reduction and cost savings

ChemPass

1:40 – 2:40pm

Computational Approaches in Optimizing Leads for RNA Drug Targets with a Focus on Oligonucleotides

In silico assessment of the sequence, as it relates to human and preclinical species, and on-target effects
Off-target risk assessments
How to assess chemistry modification using AI

Andrew Hoss

Associate Director, Bioinformatics
Wave Life Sciences

2:40 – 2:45pm

Closing Address

Drug Response Prediction

Time

Titles and Bullets

Facilitator

DAY 1 (May 18, 2021)

8:30 – 9:00am

Opening Address & Keynote:

Augmented Intelligence; A More Realistic Paradigm in Data Driven Drug Development?

Augmented Intelligence: emerging realism after the hype?
Eroom’s Law: the ever pressing need for data driven drug development
Data quality and machine readability: the elephant in the AI room?
Collaborative Intelligence: evolving towards ever closer human-machine interaction

Arie Baak

Co Founder
Euretos

9:10 – 10:10am

Not all AIs are Created Equal: Picking the Right Tool for the Job

What is the range of problem statements for AI solutions?
Deciding what’s more important in each scenario: Finding what you already know, or predicting something new
Can the same AI be used for benefit and risk evaluation of assets or programs?
If stuck with limiting data but great AI, can you federate access?

Aris Persidis

Co-Founder and President
Biovista

Aris Persidis is Co-Founder and President of Biovista, ranked Top-10 worldwide in healthcare AI (Forbes; CIO Bulletin; Deep Knowledge Analytics). He also served as SVP at Upstate (acquired by Serologicals for $205MM), and VP of Business Development at Serologicals (acquired by Millipore for $1.4 Billion). Aris also worked on the establishment of and received co-founder stock in Cellzome (acquired by GSK for $100 MM), Anadys (acquired by Roche for $240 MM), and RheoGene (acquired by Intrexon, $ n/a). Aris also served as Assistant Professor (Adjunct) and also Assistant Director, Medical Center Technology Transfer Program at Wharton. Other notables: In 2020, included in the list of the world’s top 50 Futurists (AWS Institute – Toffler Foundation – After Shock); Authored the Industry Trends series for Nature Biotechnology; Awarded the Honeywell Futurist Award; Boards: Biovista, MBF Therapeutics; Ph.D. Biochemistry Cambridge University.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

1-2-1 Meetings

1:40 – 2:40pm

ADMET Simulation and Drug Dosage Prediction Using AI/ ML

What is the promise and limitation of current approaches to simulate ADMET in silico?
Which aspects of ADMET can be currently modeled mechanistically vs. empirically vs. neither?
What types of experiments or data would help to address blind spots in our understanding and ability to predict ADMET?
How can AI/ML narrow the translation gap between preclinical models and human?

Jenny Zhang

Director, Genomic Data Science
Immuneering

Jenny joined Immuneering in 2016, where her team develops the deep learning algorithms underlying Immuneering’s small molecule screening platform, which is used to accelerate drug discovery across pipelines in oncology, immuno-oncology, and neurodegenerative diseases. She graduated from MIT with a B.S. in Physics, and Duke with a Ph.D. in Genetics and Genomics. In her doctoral work in Sandeep Dave’s lab, she developed novel experimental and bioinformatic methods to extract genetic material and clinically relevant mutational insights from 1000 lymphoma cases.

DAY 2 (May 20, 2021)

8:00 – 9:00am

Opening Address & Keynote Presentation:

Using Causal Data to Empower Drug Discovery

Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery

Daniel Jamieson

CEO and Founder
Biorelate

9:10 – 10:10am

Achieving Efficacy and Safety In Vivo Using AI- What are We Missing?

Explore the current gaps and opportunities for computational algorithms to in vivo relevance
Biological data is often driven by feasibility/technology push, and not by need/scientific pull – how do we align both?
Discuss if new types of data and technologies should be evaluated independently by individual companies, or collaboratively to de-risked in vivo transition
Explore the current capabilities of AI in bridging hazard to risk and assays to organ risk assessments

Yves Nanfack

Associate Director, Head of Structure and Digital Drug Discovery
EMD Serono

Dr. Fomekong Nanfack is Associate Director at EMD Serono, a pharmaceutical company, a division of Merck KGaA, Darmstadt, Germany. Dr. Fomekong Nanfack is leading the “structural and Digital Drug Discovery” group, an interdisciplinary team of scientists built around a core of experts to drive drug discovery of biologics. Yves has contributed significantly to the company in several key areas including antibody discovery, establishing in silico methods and pipeline to accelerate hits selection and optimization as well as introducing AI methods for large molecules.

Prior to joining EMD Serono, Dr. Fomekong Nanfack was located in Geneva, Switzerland at Merck Serono, where he held a Computational Biologist position focusing on antibody engineering and bioinformatics for molecular biology. In his early professional years, prior to joining the pharma industry, Dr. Fomekong Nanfack has worked in image processing and computational finance.

10:10 – 11:00am

BREAK

11:00 – 11:30am

1-2-1 Meetings

11:35am – 12:05pm

1-2-1 Meetings

12:05 – 1:00pm

BREAK

1:00 – 1:30pm

Target Identification Topic Euretos Poster Session

AI Driven Insights for Target Discovery and Indication Expansion

During this session we will explain how we use AI technologies, such as machine reading and learning, to create data driven disease insights into the biological systems and molecular mechanisms that drive phenotypes, disease pathology, toxicity and drug response. For target identification we perform a perturbation analysis of a model of the disease of interest to identify high impact targets. In indication expansion we use perturbation analysis to assess for thousands of pre-calculated disease models how effective modulation of a target of interest will be.

Euretos

1:40 – 2:40pm

AI Prediction of Drug Responses in Patients Using Biomarkers and Genomics

Discuss state-of-the-art machine learning powered approaches and success stories to analyze OMICS biomarkers and redefine patient stratification models
Explore Real World Evidence (RWE), mining unstructured text, semantic integration and leveraging several publicly available information data assets are powering the next generation biomarker identification
Understanding limitations and caveats when Using AI/machine learning predictions
Ranga Chandra Gudivada, Director AI Innovation, Strategy & Partnerships, Advanced Analytics & Data

LiMing Shen

Director of Translational Informatics
Sanofi

Dr. LiMing Shen works in the translational medicine and computational modeling space. He has broad experiences across the whole spectrum of Pharma R&D. At Sanofi, he led the exploration of using RWE to improve clinical trial design and execution, focusing on developing models to quantitatively assess patient risk profiles and sampling bias in the clinical trial population, which in turn can be applied to evaluate inclusion/exclusion criteria, augment efficacy prediction and other scenarios. Dr. Shen obtained his Ph.D. in Neuroscience from the University of Minnesota, and his BS in Physics from Fudan University.

2:40 – 2:45pm

Closing Address

Augmented Intelligence in Drug Discovery XchangeEast Coast 2021May 18 & 20

Data Quality

Partners

Augmented Intelligence in Drug Discovery Xchange
East Coast 2021
May 18 & 20