February 27-28, 2019
San Francisco

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Speakers

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Tom Chittenden, PhD, DPhil, PStat
Vice President for Statistical Sciences, Founding Director, Advanced Artificial Intelligence Research Laboratory
WuXi NextCODE

Day One

Wednesday 27th February, 2019

08.40 | A.I. and Precision Medicine: Furthering Scientific Understanding of Complex Disease

Kim Branson
Head of A.I (ECDi)
Genentech

Kim has been involved in large scale machine learning and medical informatics initiatives for over 15 years, over a range of ventures from computational drug design to disease risk prediction. He is currently the Head of the Artificial Intelligence group for Genentech, Early Clinical Development.  Kim received degrees from the University of Adelaide (Science and Medicine), and a PhD from the University of Melbourne (Australia) He was a Peter Doherty fellow and received postdoctoral training at the University of Cambridge, and Stanford University (Dr Vijay Pande).  He then held leadership and consulting roles in the pharmaceutical and medical informatics industry. Kim began his industry career at Vertex Pharmaceuticals (Pat Walters) Following this, Kim worked as the founding team for Discovery Engine (acquired by Twitter in 2009) and health informatics at Gliimpse (acquired by Apple in 2017). Recently, he served as founder and Chief Data Scientist at Lumiata, a predictive health analytics company. Kim currently serves on the board of OpenEye Scientific Inc.

Day One

Wednesday 27th February, 2019

09.10 | Projects Colossus and Enigma: The Use of AI Methods in Early Drug Discovery and Late Drug Development

Sandor Szalma
Global Head of Computational Biology
Takeda

Sándor Szalma is Global Head, Computational Biology in Takeda Pharmaceuticals. He is responsible for computational biology, computational and statistical genetics, machine learning and informatics approaches supporting target discovery/forward translation and reverse translation/biomarker and patient stratification in oncology, neuroscience and gastroenterology. He serves as a member of the governance board of Open Targets and leads the Takeda engagement in the Regeneron Whole Exome Sequencing of UK Biobank Consortium. Before joining Takeda, he was head of Translational Informatics and External Innovation, R&D IT in Janssen Research & Development, LLC. Previously, he was member of the industry advisory committee of ELIXIR, member of the board of the Pistoia Alliance, member of the Translational Medicine Advisory Committee of the PhRMA Foundation and led the Data & Knowledge Management Strategic Governance Group of Innovative Medicine Initiative. His past positions included president of MeTa Informatics, general manager of QuantumBio and senior director of Computational Biology and Bioinformatics at Accelrys, Inc. He was co-founder of Acheuron Pharmaceuticals, Inc. He lectured at UCSD Extension and was adjunct professor at Rutgers University in the Computational Biology and Molecular Biophysics program. He is the author of 45 scientific publications and book chapters and two patents. He received his doctoral degree in physical organic chemistry from A. Szent-Györgyi Medical University in Szeged, Hungary.

Day One

Wednesday 27th February, 2019

12.00 | Insights from Machine Learning in Support of Reverse Translation

Lei Jia
Senior Scientist
Amgen

Lei Jia is currently a Sr. Scientist at Amgen focusing on data science related predictive modeling in small molecule and biologics. He has developed multiple applications to accelerate Amgen drug discovery process. Before joining Amgen, Lei has been a Scientist in molecular modeling and protein engineering at Pacific Biosciences Inc. to develop next gen DNA sequencing platform, as well as a Sr. Protein Engineer at Intrexon to develop protein engineering informatics applications. Lei pursued his Ph.D. and conducted his postdoctoral training with Prof. Suse Broyde in computational chemistry and structural biology at New York University. During 2 summers of his Ph.D. study, Lei also received industrial training at Hoffmann-La Roche, where he developed machine learning applications for small molecule drug discovery.

Day One

Wednesday 27th February, 2019

14.00 | Implementation of Deep Learning Based ADME Predication in Small Molecule Drug Discovery Pipeline

Ji Ma
Principal Scientist
Amgen

Ji Ma, Ph.D., Amgen Inc.: South San Francisco, CA Dr. Ma is Principal Scientist, Department of Pharmacokinetics and Drug Metabolism, Amgen Inc., South San Francisco, CA. He received his B.S. in Chemistry from Wuhan University, China in 1993, and a Ph.D. in Medicinal Chemistry from a joint program with China Pharmaceutical University and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences in 1998. In 2001, he joined Tularik Inc. (acquired by Amgen Inc. in 2004) after three years postdoctoral research at the University of Virginia. In addition to his project representation role for PKDM function, his research focus has been on application of cutting edge technologies to support small molecule and biologics drug discovery and development, including metabolite/catabolite identification, bioanalysis, automation of sample preparation, as well as in silico ADME platforms that may accelerate the discovery processes.

Day Two

Thursday 28th February, 2019

10.00 | Lessons Learned From Small Molecule Drug Discovery Context – Faster and Better Decisions by Virtual ADME Tools

Benjamin Adams
Consultant Biologist
Eli Lilly

Benjamin L. Adams is a seasoned pre-clinical drug discovery scientist at Eli Lilly & Co. His behavioral pharmacology research has spanned multiple sub-disciplines of Neuroscience Drug Discovery and Development, including pain, addiction and cognition.  He has automated a number of behavioral and tolerability endpoints, while also developing and delivering novel Artificial Intelligence platforms for behavioral and imaging studies.  Ben has won multiple innovation awards for this work, acknowledging both the novelty and future potential for these drug discovery platform advancements. He received a BS in Biological Psychology from Western Illinois University and a MS in Neuroscience from Purdue University.

Day Two

Thursday 28th February, 2019

14.30 | Applying Artificial Intelligence to Drug Discovery: A Lesson in Success (and Frustration) For Automating In Vivo Assays

Edward Painter
Founder & CEO
A2A Pharmaceuticals

Edward Painter is the CEO and Founder of A2A Pharmaceuticals. A2A Pharmaceutical's pipeline consists of new drug candidates focused on mixed-lineage leukemia (MLL) and YAP/TEAD solid tumor mechanisms of action. He is also a founder of 2 other companies, Biomea Fusion is a spin-off of A2A focused on Leukemia and Drug Resistant Bacterial Infections while  Consortium AI is focused on Duchenne’s Muscular Dystrophy and other rare diseases. Edward studied for his Bachelors at Illonois Wesleyan University and went on to obtain an MBA in finance at Gies College of Business, University of Illinois.

Day Two

Thursday 28th February, 2019

11.30 | Computational Drug Design Engine by A2A Pharmaceuticals

Rafael Depetris
Principal Scientist
Kadmon Corporation

Rafael obtained his Ph.D. from NYU in 2008 working with Stevan Hubbard in the field of X-Ray crystallography, specifically focusing on the structural analysis of proteins that interact with the insulin receptor. His post-graduate work was done at Weill Cornell Medical College where he focused in the structural analysis of the HIV-1 Envelope proteins as well as of the HIV-1 neutralizing antibodies. Rafael went on to assume a position of a Senior Scientist at Pfizer CTI in New York, where he initiated his work focused on modeling of targets and therapeutic antibody candidates. He was also commanding the efforts for the humanization of the lead antibody candidates based on the modeling analyses. Rafael is continuing to explore structural features of biologics and their targets in his current position as a Principal Scientist at the Kadmon Corporation in NYC. At Kadmon he is combining his expertise in structural biology with in silico approaches to drive forward discovery projects. He has participated in many symposia, speaking about the interplay of structural analysis and computational tools.

Day Two

Thursday 28th February, 2019

12.30 | Machine Learning Descriptors for Epitope Mapping and Affinity Prediction: A New Frontier in Structural Analysis

Marcin von Grotthuss
Senior Computational Scientist
The Broad Institute

Marcin von Grotthuss is a Senior Computational Scientists at the Broad Institute of MIT and Harvard. He earned the Ph.D. in bioinformatics at Radboud University Nijmegen, Netherlands and gained his professional experiences at: Sanford-Burnham Institute (La Jolla, CA), University of Washington (Seattle, WA), Harvard University (Cambridge, MA), University of Cambridge (England, UK), University of California Irvine (Irvine, CA), and The Brigham and Women’s Hospital / Harvard Medical School (Boston, MA). In his work, initially, he applied machine-learning technics to estimate biomedical properties of small molecules. Next, he worked on predicting protein structures from sequences and protein functions from sequences and/or structures.  After mastering it, he was focused on D. melanogaster genomics as well as human genomics as a part of 1000 Genomes Project. Recently, he develops knowledge portals for complex traits like type 2 diabetes, stroke, and cardiovascular diseases. And he is one of the instrumental scientists in the consortium that builds a Biomedical Data Translator.

Day Two

Thursday 28th February, 2019

14.00 | Toward a Universal Biomedical Data Translator: from Vision to the Working Prototype

Slava Akmaev
Senior Vice President, Chief Analytics Officer
Berg

Slava Akmaev, Ph.D., is the Senior VP and Chief Analytics Officer at BERG.  Dr. Akmaev is the industry leader in AI and machine learning applications in life sciences and healthcare. He heads the BERG Analytics business division and is the chief architect of Bayesian Artificial Intelligence software, bAIcis®.  Dr. Akmaev is an avid advocate for data driven research and promotes wider utility of advanced mathematical modeling techniques in pharmaceutical and clinical research. Prior to his role at BERG, Dr. Akmaev spent a decade at Genzyme R&D and the CLIA diagnostic laboratory, Genzyme Genetics. He led the development and commercialization of novel multi-omic panel diagnostics in oncology and prenatal medicine. Dr. Akmaev has published numerous peer-reviewed manuscripts in genomics, systems biology, biochemistry and human genetics. He is a frequent speaker at AI, analytics and precision medicine conferences.  Dr. Akmaev holds a Ph.D. in Applied Mathematics from the University of Colorado at Boulder.

Day One

Wednesday 27th February, 2019

11.30 | Starting from Scratch: Data Driven AI in Target Discovery

Pat Walters
Computation & Informatics group
Relay Therapeutics

Pat Walters heads the Computation & Informatics group at Relay Therapeutics in Cambridge, MA. His group focuses on novel applications of computational methods that integrate computer simulations and experimental data to provide insights that drive drug discovery programs. Before joining Relay, he spent more than 20 years at Vertex Pharmaceuticals where he was Global Head of Modeling & Informatics.   Pat is a member of the editorial advisory board for the Journal of Medicinal Chemistry, and previously held similar roles with Molecular Informatics, and Letters in Drug Design & Discovery.   He continues to play an active role in the scientific community.  Pat was the Chair of the 2017 Gordon Conference on Computer-Aided Drug Design.  He has been instrumental in many community-driven efforts to evaluate computation methods including the NIH funded Drug Design Data Resource (D3R) and the American Chemical Society TDT initiative.  Pat received his Ph.D. in Organic Chemistry from the University of Arizona where he studied the application of artificial intelligence in conformational analysis. Before obtaining his Ph.D., he worked at Varian  Instruments as both a chemist and a software developer. Pat received his B.S. in Chemistry from the University of California, Santa Barbara.

Day One

Wednesday 27th February, 2019

09.40 | Integrating Machine Learning into the Drug Discovery Workflow

Brandon Allgood
CTO
Numerate

Brandon Allgood is the CTO and cofounder at Numerate, Inc, an AI driven drug discovery company. At Numerate, he manages the development and application of Numerate's AI platform and is responsible for Numerate’s technological vision. Brandon has also served as Director of Computational Science at Numerate and as a Research Scientist at Pharmix. He received a B.S. in Physics from the University of Washington, Seattle, and a Ph.D. in Computational Physics from the University of California, Santa Cruz. Brandon has authored scientific publications in astrophysics, solid-state physics, and computational biology and has 15 years of experience in AI, mathematical modeling, and large scale cloud and distributed computing. He is a member of the Forbes Technology Council and is a UCSC Foundation Trustee.

Day One

Wednesday 27th February, 2019

10.10 | Harnessing the Power of AI to Improve Translation: From Biology to Discovery & Discovery to Drug Product Development

Guido Lanza
President & CEO
Numerate

Guido Lanza is the President and CEO of Numerate. Formerly, Guido was the co-founder and Chief Technical Officer of Pharmix, where he served on the company’s Board of Directors for five years and was named one of Business Week’s “Tech’s Best Young Entrepreneurs under-30” in 2006. Prior to Pharmix, Guido was a research scientist with Professor Koza of Stanford University with whom he developed applications of genetic programming in the area of bioinformatics and computational biology. Guido is the author of 10 scientific publications and inventor of 4 issued patents. He received a B.A. in Molecular and Cell Biology and Integrative Biology from the University of California, Berkeley and an M.Sc. in Bioinformatics from the University of Manchester (UK).

Day Two

Thursday 28th February, 2019

09.30 | Panel Discussion: Collision of Science & AI: How Traditional Drug Developers Need to Work Together Effectively with AI Pioneers

David Wild
Founder & President,
Data2Discovery

David is an Associate Professor at Indiana University School of Informatics and Computing, where he is Director of Data Science Academic Programs, overseeing over 500 graduate students in data science. He leads the Integrative Data Science laboratory, and is Founder and President of Data2Discovery Inc. His research interests include integrative data science - data science that brings together heterogeneous data sources and skill sets to tackle complex problems; data science for healthcare; cheminformatics; network chemical biology; informatics in disasters and emergency response; and data privacy and security. He completed a B.Sc. in Computing Science at Aston University, Birmingham, England in 1991, and a Ph.D. in Computational Drug Discovery at Sheffield University, England in 1994. He worked for several years in scientific computing in the pharmaceutical industry. In 2004, he moved into Academia to form new academic research and educational programs at Indiana University. He has approximately 100 research publications, and is a founding editor of the Journal of Cheminformatics. He has been PI or CoPI on $4m in research funding. Currently, David is Director of Data Science Strategic Initiatives and leads the Integrative Data Science Lab at Indiana University. He continues as President of Data2Discovery Inc, and heads the leading-edge, D2D Innovation Labs.

Day One

Wednesday 27th February, 2019

12.30 | Reinventing Drug Discovery with AI

Pankaj Agarwal
Senior Fellow, Computational Biology
GSK

Day Two

Thursday 28th February, 2019

12.00 | The Role of AI and Machine Learning Technologies to Empower GSK’s Level of Drug Discovery Innovation

Kevin Hua
Senior Manager, AI & Machine Learning Development
Bayer

Dr. Kefeng (Kevin) Hua has over 20 years of experience working in research, design and development of AI/Machine Learning applications in various industries. He is currently a Senior Manager with the Digital Health Intelligence group of Bayer, responsible for application of AI to clinical trials and digital health innovation. Prior to Bayer, he was a machine learning specialist at Deloitte Analytics Institute/AAM Group and a research manager of AI at Center for Advanced Research of PwC. He holds a Ph.D in AI from Swiss Federal Institute of Technology (EPFL). His work in analytics won the Analytics Leadership Award from IU Kelley School of Business in 2014 and INFORMS Data Mining contest in 2008.

Day Two

Thursday 28th February, 2019

10.30 | Potential AI Applications in the Whole Drug Discovery & Development Process

Friedrich Rippmann
Director, Computational Chemistry & Biology, Global Research & Development | Discovery Technologies
Merck

Day One

Wednesday 27th February, 2019

14.45 | The Practical Impact of AI in Drug Discovery So Far

Jie Fan
CEO
Accutar Biotech

Dr. Fan was trained at UC, Berkeley in Biostatistics and obtained his Doctor degree from Cornell/MSKCC in structural biology/Immunology. He was further trained by Dr. Gunter Blobel at Rockefeller University. With a dream of using a hybrid approach (by combining computation design and experimental validation) to accelerate drug discovery, and to reform current “hit-2-lead” drug discovery scheme, Dr. Fan founded Accutar biotech with the support of Dr. Gunter Blobel. Dr. Fan also hold a joint appointment at SUNY, Downstate medical school as a research assistant professor.

Day One

Wednesday 27th February, 2019

14.30 | AI Empowered Drug Discovery

Lina Nilsson
Senior Director of Data Science Product
Recursion Pharmaceuticals

Lina Nilsson is Senior Director of Data Science Product at Recursion Pharmaceuticals, a company that is reimagining drug discovery through artificial intelligence and large-scale experiment automation. Previously, she was the COO of Enlitic, a startup that uses deep learning to improve clinical diagnostics in medical imaging. Before this, she was the Innovation Director at one of the largest centers at the University of California, Berkeley, where she helped scale and spin out new social-impact technologies. Lina has been recognized on MIT Technology Review's "TR35" annual list of the world's top innovators for her work in open science. Her writings have been published in the New York Times, Washington Post, Science, and Make Magazine. Lina has a PhD in biomedical engineering from the ETH Zurich.

Day Two

Thursday 28th February, 2019

15.00 | Extending Machine Learning Tools to Increase Drug Discovery Pipeline Robustness and Improve the Chances of Translational Success