AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EAST COAST 2023
Boston
May 25, 2023

Welcome to hubXchange’s Augmented Intelligence (AI) in Drug Discovery Xchange East Coast 2023, 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.

Please note this is an In-Person meeting.

VENUE DETAILS: Hilton Boston Woburn Hotel, 2 Forbes Road, Woburn MA 01801

SNAPSHOTS OF DISCUSSION TOPICS

Track 1: Data Quality

Overcoming current limitations in data generation and management
Guidance on standard practice in clinical genomic data generation and data analysis pipelines

Track 2: Target Identification

Use of AI in integrating multi-dimensional datasets for target discovery
Emerging modeling approaches to identify the covalently druggable targets

Track 3: Lead Generation

Bridging the gap between biological nails and AI hammers
Molecular design cycle: computation-first approach

Track 4: Lead Optimization

Challenges and approaches to building deep learning models for antibody lead optimization

Track 5: Drug Response Prediction

Can machine learning methods provide insights and predictions for cancer drug response?

Full Xchange Agenda

Click on each track for detailed agenda

Data Quality

Time

Titles and Bullets

Facilitator

8:00 – 8:30

Registration

8:30 – 9:00

Opening Address & Keynote Presentation
Building predictive models on rock and not quicksand: solid data foundations

Most if not all cutting-edge predictive models we see appear in Data Science and Life Science R&D are built on data. The challenge which is often overlooked is how to make sure the data foundations for these models are of high quality, reliable, normalized, and re-usable. This talk will focus on the experiences and processes needed in making both Elsevier and customer internal data “machine-learning ready”. Surprisingly, this often not only involves changes in data capture and modelling, but also changes in people and processes.

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

In Elsevier’s Professional Services team, Frederik leads the global consultancy practice on data integration and analytics projects throughout the life science, chemistry and engineering domains using commercial, proprietary, and public data sources. He holds a doctorate in Chemical Physics from the University of Amsterdam / FOM Institute AMOLF and a master’s degree in Chemistry from Utrecht University.

09:05 – 10:05

Guidance on standard practice in clinical genomic data generation and data analysis pipelines

Vendor Qualification and fit for purpose assay selection
Global regulatory guidance for CRO based data generation
Integration of new technologies in clinical research

Budhaditya Banerjee

Senior Scientist, Takeda

After finishing his postdoctoral training in Harvard Med School, Genetics dept. Banerjee has been navigating through genomics research both at the biotech industry and Pharmaceutical organization setting and has gained significant knowledge of the various facets of drug development process. He spent a few years as a Program co-Lead and Tech R&D lead in several drug programs in Cellarity, Inc. There he lead a team of scientists to perform protocol optimization and data generation efforts using fit for purpose technology /tool-kits.

Currently, he’s the Genomics subject matter expert (SME) for preclinical and clinical drug development workflows at Takeda Pharmaceuticals as a member of the Biomarker Science and Technologies team in PTS. He is an advisor for the current programs for genomics assay development to ensure fit-for-purpose assay selection and development for clinical biomarker assessment in patients for Neuroscience, GI, Oncology and Rare disease programs.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Data quality challenges: how enabling data dogfooding helps to use and re-use data in AI Drug Discovery.

Capturing data with model building / re-use in mind
Mapping, harmonizing and combining internal and external data sets for analytics and ML
model training
The role of ontologies in data harmonization
Levels of trust in quality of internal and external/public data

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

In Elsevier’s Professional Services team, Frederik leads the global consultancy practice on data
integration and analytics projects throughout the life science, chemistry and engineering domains using commercial, proprietary, and public data sources. He holds a doctorate in Chemical Physics from the University of Amsterdam / FOM Institute AMOLF and a master’s degree in Chemistry from Utrecht University.

12:20 – 13:20

Networking Lunch

13:20 – 13:50

1-2-1 Meetings / Networking Break

13:50 – 14:20

1-2-1 Meetings / Networking Break

14:25 – 14:55

Poster Session
Unlocking Biomedical Data for AI / ML using Large Language Models (LLMs)

To effectively train predictive models in drug discovery, large volumes of clean and linked data are required, which can be a costly and time-consuming task to curate manually. As such, there is a growing need for automated curation processes that can accurately and efficiently label data at scale.
Elucidata has developed a biocuration process that leverages domain-trained BERT-like models for a variety of information extraction tasks: Identification of cell type, cell line, tissue, disease, and other characteristics from unstructured biomedical datasets. This approach has shown promising results in improving the quality and efficiency of data curation.
In this session, we will explore the specifics of Elucidata’s NLP-based biocuration and how it can help R&D teams make their data interoperable, enable data and metadata integration and generate model quality datasets for AI/ML use cases.
As a bonus, we will also demonstrate our experiments with Open AI’s Chat-GPT and its potential in solving edge cases in biocuration.

Mya Steadman

Solutions Architect, Elucidata

Mya (my-uh, not me-uh) Steadman is a Bioinformatics Scientist at Elucidata, working with data scientists at therapeutic and diagnostic companies to identify and resolve roadblocks in their research due to unclean biomedical data. Before Elucidata, Mya was a metabolomics scientist studying Cancer and Autoimmune diseases at several biotech start-ups in Cambridge, Massachusetts, including Agios Pharmaceuticals. While at Agios, she had the chance to work with Elucidata’s founder Abhishek Jha, who taught her that the value of your results is determined by the quality of your data.

16:00 – 16:15

Afternoon Refreshments

16:15 – 17:15

Data quality, variability and integrity: how do we achieve it?

What data are you using? What’s the most challenging problem for data quality in your job? How do you ensure high data quality?
Key issues: messy public data/metadata, raw data are not available, data are not processed in a consistent way

Ming Tang

Director of Computational Biology, Immunitas Therapeutics

Ming “Tommy” Tang is the Director of Computational Biology at Immunitas. Prior to joining Immunitas Tommy was at Dana-Farber Cancer Institute and Harvard University, where he led a team to analyze immune-oncology related single-cell sequencing datasets and spearheaded an NIH-funded project called Cancer Immunological Data Commons. Tommy has a wealth of experience as a computational biologist with over ten years in analyzing large-scale (epi)genomic/transcriptomic data and automating the analysis by using workflow languages such as Snakemake.. Prior to joining Dana-Farber, Tommy received his Ph.D. in Genetics and Genomics from the University of Florida and completed a three-year postdoc at MD Anderson. He has a keen interest in teaching computational skills to wet biologists and is a certified instructor for Data Carpentry.

17:15 – 18:15

Evening Drinks Reception

Target Identification

Time

Titles and Bullets

Facilitator

08:00 – 08:30

Registration

08:30 – 09:00

Opening Address and Keynote Presentation
Building predictive models on rock and not quicksand: solid data foundations

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

09:05 – 10:05

Use Of AI in integrating multi-dimensional datasets for target discovery

Areas where AI is applied in drug target selection
Role of AI in integration and analysis of multi-OMICS data
Machine learning for gene prioritization using genetic evidence data
Application of AI in literature analysis
Challenges and limitations in using AI methods in target discovery

Lakshmi Kuttippurathu

Associate Director, Computational Biology & Data Sciences, Lexicon Pharmaceuticals

Lakshmi Kuttippurathu is an accomplished interdisciplinary scientist with 15+ years of experience in Computational Biology. During her research career at Harvard MIT Health Science and Technology, Lakshmi developed a computational tool to study regulatory dynamics of transcription factors. As a postdoctoral fellow and later as a Faculty at Thomas Jefferson University, she contributed to the field of Liver regeneration and Neuroscience with focus on understanding regulatory network dynamics driven by perturbations, using Systems Biology approach. Currently, she is the Associate Director, Computational Biology and Data Sciences at Lexicon Pharmaceuticals, where she is spearheading the computational efforts on developing and implementing a strategy for preclinical target discovery.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Disrupting Disease Landscapes: AI-Driven Insights from Large Biomedical Datasets

AI’s impact on complex disease research: Discuss how artificial intelligence and advanced machine learning techniques transform human health, genetic, and rich phenotypic data analysis.
Shifting to longitudinal studies: Explore the integration of AI and dynamic systems theory into very large models of human health to enable comprehensive causal inference, understanding of time scales, reversibility, and intricate relation between aging and complex diseases.
Connecting molecular biology and disease understanding: Examine the role of real-world, longitudinal data in deepening our understanding of complex diseases and informing treatment strategies.
Advancing drug discovery and development: Assess the prospects for novel therapeutic targets, emerging therapeutic modalities, and generative chemistry-driven pipelines that lead to groundbreaking drugs and improved healthcare.

Peter Fedichev

Chief Executive Officer and Co-founder, Gero

Ph.D. from the University of Amsterdam. Co-founder of Gero, a data-driven longevity biotech company, that develops new drugs against aging and other complex diseases using AI-platform. An author of 75+ published papers in multiple domain areas, including publications in Science and Nature Communications.

12:20 – 13:20

Networking Lunch

13:55 – 14:25

1-2-1 Meetings / Networking Break

14:25 – 14:55

1-2-1 Meetings / Networking Break

15:00 – 16:00

Emerging modeling approaches to identify the covalently druggable targets

To predict cryptic pocket and protein conformation states in high throughput
To evaluate residue reactivity accurately & efficiently
Modeling strategy to mine new targets

Yuan Hu

Principal Scientist, Frontier Medicines

Dr. Yuan Hu is a Computational Chemist in Frontier Medicines at Boston site. Before that, he worked at Alkermes and Merck with molecules he designed being advanced into clinical trial. He has been a chemistry team leader and has substantial modeling knowledges in new target identification, hit finding, lead generation, and lead optimization with expertise in molecular dynamics, FEP/TI, generative modeling, reinforcement learning, quantum mechanics, and workflow automation for both small molecule and biologics drug discovery. He received his Ph.D. in Computational Chemistry from University of Delaware. Additionally, he has a M.S. Degree in Organic Chemistry.

16:00 – 16:15

Afternoon Refreshments

16:15 – 17:15

Exploring key issues in drug target identification

Complexity of disease mechanisms
Lack of understanding of disease biology
Limited availability of suitable drug targets
High failure rate
Ethical and legal considerations

Andac Demir

Data Scientist, Novartis

Andac Demir received the B.S. degree in Electrical Engineering from Tufts University, Medford, MA, USA in 2017, and the Ph.D. degree in Electrical Engineering with Northeastern University, Boston, MA. He worked as a Research Intern at Schlumberger-Doll Research Center in Cambridge, MA, and in 2020, he worked as a Research Intern at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA. In 2022, he joined Novartis Institutes for BioMedical Research. Andac Demir has published several papers on topics such as ligand-based virtual screening, molecular property prediction, and tumor segmentation in top-tier AI/ML conferences including NeurIPS, ICLR, and CVPR. His research interests include geometric deep learning, topological data analysis, graph convolution, and adversarial robustness.

17:15 – 18:15

Evening Drinks Reception

Lead Generation

Time

Titles and Bullets

Facilitator

08:00 – 08:30

Registration

08:30 – 09:00

Opening Address & Keynote Presentation
Building predictive models on rock and not quicksand: solid data foundations

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

09:05 – 10:05

Applying AI methods to lead generation

How can we effectively navigate the billions of molecules available through synthesis on demand?
Does generative chemistry provide a reliable route to hit identification?
Can deep learning impact structure-based virtual screening?
How will protein structure prediction impact hit identification?
How can machine learning interact with methods like DEL screening?

Patrick Walters

Chief Data Officer, Relay Therapeutics

Pat Walters is Chief Data Officer at Relay Therapeutics in Cambridge, MA. Prior to joining Relay, he spent more than 20 years at Vertex Pharmaceuticals where he was Global Head of Modeling & Informatics. Pat is the 2023 recipient of the Herman Skolnik Award for Chemical Information Science from the American Chemical Society. He is a member of the editorial advisory boards for the Journal of Medicinal Chemistry and the Journal of Chemical Information and Modeling. He is co-author of the book “Deep Learning for the Life Sciences,” published in 2019 by O’Reilly and Associates. Pat received his Ph.D. in Organic Chemistry from the University of Arizona where he studied the application of artificial intelligence in conformational analysis.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Molecular design cycle: computation-first approach

How can computational methods be used to generate novel lead molecules for drug discovery?
What are some of the most effective strategies for identifying potential lead molecules using in silico techniques?
How can machine learning and artificial intelligence be applied to the lead molecule generation process in drug discovery?
What are some of the current limitations and challenges in using computational tools for lead molecule generation in drug discovery?
How can we integrate experimental data and validation into computational lead molecule generation to improve hit-to-lead conversion rates?

Shivam Patel

Head of Data Sciences and Machine Learning, Psivant Therapeutics

Shivam Patel is an experienced and skilled professional with a background in Data Science and experience working at the interface of computation and laboratory in drug discovery. He holds a Master’s degree in Data Science from Northeastern University and has over three years of industry experience. In January 2020, Mr. Patel joined Silicon Therapeutics as a Research Associate, where he was part of the team that discovered the clinical compound SNX281 STING agonist. He later joined Roivant Sciences as Senior Data Scientist following the acquisition of Silcontx. Currently, he holds the position of Head of Data Sciences and Machine Learning at Psivant Therapeutics, where he is responsible for generating and evaluating large libraries of synthesizable small molecules.

12:20 – 13:20

Networking Lunch

13:20 – 13:50

1-2-1 Meetings / Networking Break

13:50 – 14:20

1-2-1 Meetings / Networking Break

14:25 – 14:55

Poster Session
Fast and Affordable: An AI-based platform for high-throughput virtual screening and molecule generation with cloud scalability

Our validated fast screening platform utilizes cutting-edge technologies to enable high-throughput virtual screening of billions of molecules in just two weeks at a low cost in the AWS cloud. The platform leverages a representative subset of the database to dock against a given target and is further validated with the PELE Monte Carlo algorithm. We use this data to train a surrogate model that can efficiently predict binding scores for the entire database. The platform incorporates an active learning approach optimized for retraining the model, enabling it to explore larger regions of the space more accurately. In addition, two generative models, one designed to generate great molecular diversity and another capable of producing high-quality variations of the best molecules offer alternatives to already saturated IP spaces. This platform offers a powerful tool for efficient screening of large databases and holds promise for accelerating drug discovery.

Alexis Molina

Director of AI, Nostrum Biodiscovery

With a research background in protein folding and drug-drug interaction prediction, he joined Barcelona Supercomputing Center in 2019 to work with Prof. Victor Guallar on developing generative AI algorithms for molecules and proteins. As the current AI research lead at Nostrum Biodiscovery, his focus is on generative AI and ultra-large database screenings for drug discovery

15:00 – 16:00

Bridging the gap between biological nails and AI hammers

How do you identify the parts of your pipeline where AI has the potential to address unmet scientific needs?
To what extent do your digital and bench teams collaborate on joint priorities?
What ways have you found to explore and validate unproven digital approaches before investing heavily in them?
How do you balance your data teams’ time between focused work and communication with wet lab colleagues?

Eric Ma

Principal Data Scientist, Moderna

Eric is a Principal Data Scientist at Moderna, leading the Data Science and AI (Research) team. Previously, he worked at Novartis Institutes for Biomedical Research and was an Insight Health Data Fellow. He completed his doctoral thesis in Biological Engineering at MIT. Eric is an open-source software developer, leading projects like pyjanitor and nxviz, and contributes to various projects. His personal motto is from Luke 12:48.

16:00 – 16:15

Afternoon Refreshments

17:15 – 18:15

Evening Drinks Reception

Lead Optimization

Time

Titles and Bullets

Facilitator

08:00 – 08:30

Registration

08:30 – 09:00

Opening Address & Keynote Presentation
Building predictive models on rock and not quicksand: solid data foundations

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

09:05 – 10:15

Analyzing optimization for specific needs in dependency multiparametric optimization

Extremely large sequences space to explore
How to co-optimize multiple properties
More rounds for experimental characterization or more sequence to screen
How to avoid immunogenicity

Yu Qiu

Head of AI Innovation for Antibody, Sanofi

Yu joined Sanofi in March 2017 as a Senior Scientist in the Protein Engineering group of Biologics Research. Yu earned his PhD in Biochemistry in 2012, where he mainly used NMR to study protein structures and dynamics. Then he moved to St. Jude Children’s Research Hospital as a postdoc where he studied a Ubiquitin-like protein conjugation cascade in autophagy by biochemistry, enzymology, crystallography and EM. At Sanofi, he solved structures of antibody-antigen complexes by crystallography and cryoEM and provides structure-based antibody engineering support to projects in multiple therapeutic areas. Since 2021, he has led efforts focusing on in silico antibody engineering and de novo design mainly by physics-based and ML-based methods.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

12:20 – 13:20

Networking Lunch

13:20 – 13:50

1-2-1 Meetings / Networking Break

13:50 – 14:20

1-2-1 Meetings / Networking Break

15:00 – 16:00

Analyzing the challenge of in vivo PK profiles prediction

Accurately predicting binding affinity. Docking is a static approximation whose results are often far from reality. Dynamics approaches are much more expensive and should result in better estimates. Unfortunately, often they don’t.
Generalizable predictive models have their limitations in terms of accuracy. Still, they have a wider applicability domain than specific models trained on a subset of chemotypes that are more accurate but have limited applicability. It relates not only to ML-based models but docking as well.
A need for activity/ADMET data for a particular target/chemotype to train a suitable model.

Petrina Kamya

Head of AI Platforms, Insilico Medicine

Petrina Kamya is currently Head of AI Platforms at InSilico Medicine and President of Insilico Medicine Canada Inc., Prior to joining InSilico, Petrina worked in Market Access, and computer-aided drug discovery and development at Certara and Chemical Computing Group respectively. Petrina holds a Ph.D., in Computational Chemistry and a BSc in Biochemistry.

16:00 – 16:15

Afternoon Refreshments

16:15 – 17:15

Challenges and approaches to building deep learning models for antibody lead optimization

Data capture & processing
Training models with on small data datasets & limited resources
Making predictions on assays and processes that have large variations
Machine learning approach to optimizing experiment designs

Xin Yu

Senior Scientist, Global Biologics Discovery, Abbvie

Xin is a senior scientist at Abbvie. Research in her group focuses on building the solutions from assay design, data capture, and machine learning, such as cell line engineering & assay development to support antibody screening funnels, developing full-stack applications for capture & processing of unstructured data from assays, and optimization of architecture to facilitate rapid development of ML models.

17:15 – 18:15

Evening Drinks Reception

Drug Response Prediction

Time

Titles and Bullets

Facilitator

08:00 – 08:30

Registration

08:30 – 09:00

Opening Address & Keynote Presentation
Building predictive models on rock and not quicksand: solid data foundations

Frederik van den Broek

Senior Director Professional Services and Consulting, Corporate R&D, Elsevier

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Can machine learning methods provide insights and predictions for cancer drug response?

Patient stratification – from cox regression to deep learning – What methods/strategy should we develop to maximize the utilization of data (public and internal) to find predictive biomarkers and predict response/resistance?
Integration of clinical, demographic and molecular features – from patient health to cancer biology – what challenges do we face and how should we approach them?
The flood of molecular data coming from novel high-throughput technologies – how do we make best use of them to inform drug discovery? How do we choose which one to use and when?

Etai Jacob

Senior Director, Head of Data Science and AI, Early Oncology, AstraZeneca

Etai is an R&D senior director with more than 15 years of experience in developing and applying computational biology and ML/AI in drug discovery and early clinical development. His group at AZ specializes in data science and ML/AI research for ‘omics and clinical bioinformatics for drug discovery, translational medicine, and early clinical trials. Etai holds a PhD in Computational Biology from Bar-Ilan University and the Weizmann Institute of Science (in collaboration), MSc in Computational Biology and BA in Computer Science.

12:20 – 13:20

Networking Lunch

13:20 – 13:50

1-2-1 Meetings / Networking Break

13:50 – 14:20

1-2-1 Meetings / Networking Break

16:00 – 16:15

Afternoon Refreshments

17:15 – 18:15

Evening Drinks Reception

AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EAST COAST 2023
Boston
May 25, 2023

SNAPSHOTS OF DISCUSSION TOPICS

Full Xchange Agenda

Data Quality

Target Identification

Lead Generation

Lead Optimization

Drug Response Prediction

Partners

© Copyright 2023 by Weblify & Webtec

AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGEEAST COAST 2023Boston May 25, 2023

SNAPSHOTS OF DISCUSSION TOPICS

Full Xchange Agenda

Data Quality

Target Identification

Lead Generation

Lead Optimization

Drug Response Prediction

Partners

AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EAST COAST 2023
Boston
May 25, 2023