AI in Drug Discovery Xchange East Coast

Target Identification

Titles and Bullets
8:30am – 9:00am

Opening Address & Keynote: A Tipping Point: Working Toward an AI-Based Drug Discovery Process – Gabriel Musso, Chief Scientific Officer, BioSymetrics

9:05am – 10:05am

Enhancing disease target knowledge
graphs by combining diverse big data
  • Exploring the history and traditional mathematical best practices for data analysis 
  • Understanding the big trends and latest efforts to create more sophisticated ways to increase the volume of variant analysis 
  • Forecasting what’s next for AI and ML for genotype target identification 

Senior Research Director
Alynlam Therapeutics 

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). 

Portrait picture of Paul Nioi
10:10am – 10:40am

1-2-1 Meetings

11:50am – 12:20pm

1-2-1 Meetings

12:20pm – 1:00pm

Lunch Break

1:00pm – 1:30pm

Poster Session: AI driven insights for target discovery and indication expansion – Euretos

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.

1:35pm – 2:35pm

1-2-1 Meetings
12:20pm – 1:00pm

Lunch Break

1:35pm – 2:35pm

Using AI and ML for cellular
deconvolution and assay development
  • Optimizing information content and assay throughput
  • Implementing cellular deconvolution on bulk RNA sequencing: understanding cellular composition and genetic associations
  • Insights from combining single cell imaging and RNA sequencing

Director AI 
Boehringer Ingelheim 

Christine Hajdin, an expert in bio and chemo informatics, she is currently a principal scientist in the computational biology group at Boehringer Ingelheim.

Christine began her career in Kevin Week’s lab where she developed a love for informatics. After graduation, she worked as a data scientist at Explorys (now IBM Watson Health Care) and then as an Investigator at Novartis.  More recently she was the first computational member for Ribometrix and Flagship Lab 63.  She has worked across disease areas and throughout the drug discovery pipeline utilizing machine learning methods to perform target ID, biomarker selection and compound optimization.  

Portrait picture of Christine Hajdin

2:40pm – 3:10pm

1-2-1 Meetings

3:10pm – 3:40pm

1-2-1 Meetings

3:45pm – 4:45pm

What is the role of machine learning and AI in drug target selection and triage?
  • Large scale or precision data generation – or? What is the definition of large? Number of subjects or number of data points per subject?
  • How many consortia is needed to achieve scale and coverage of data? Or avoid them?
  • Collaborative ML/AI or let the best innovation win?
  • Successes of applying ML/AI for selecting or triaging targets – is that a game changer or evolution?
  • What is target selection and triage will look like in 10 years?

Global Head, Computational Biology
Takeda Pharmaceuticals

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 and validation/reverse translation and forward translation/biomarker and patient stratification in neuroscience, gastroenterology, rare diseases and oncology. He serves as a member of the governance board of Open Targets and leads the Takeda collaboration with the Exome Sequencing of the 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.

Portrait picture of Sándor Szalma

4:50pm – 4:55pm

Closing Address


AI in Drug Discovery Xchange | East Coast