Augmented Intelligence in Drug Discovery Xchange
East Coast, BOSTON
mAY 19, 2022
Welcome to hubXchange’s East Coast Augmented Intelligence (AI) in Drug Discovery Xchange 2022, 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 with a hybrid option to join virtually.
VENUE DETAILS: Hilton Boston Woburn Hotel, 2 Forbes Road, Woburn MA 01801
Opening Address & Keynote Presentation
X-Chem’s ArtemisAI Platform: A Flexible Approach to AI for Drug Discovery
- Intro to X-Chem
- ArtemisAI: X-Chem’s AI platform
- Overview of ArtemisAI case studies
Senior Vice President & General Manager, X-Chem
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 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 a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women.
Vice President, Scientific Computing and Data Science, X-Chem
Marie-Aude has dedicated her career to the application of informatics in the field of drug discovery. At X-Chem, she has led a collaborative approach to generating high-quality data management solutions and a world-leading integrated suite of software tools to support X-Chem’s DNA-encoded library (DEL) platform. Having been part of X-Chem’s groundbreaking forays into machine learning on DEL, she now plays a key role in the continual expansion of X-Chem’s AI platform.
The challenges of inefficient data integration – what methods can be used to ensure the integration of more data?
- How to get good quality of data?
- Sources of raw data?
- Impact of too much or too little data?
- How to process the data for integration?
- Best approaches to design the data integration framework?
Senior Scientist, Novartis
Rohit is an initiative-taking, creative, and enthusiastic scientist/data scientist with profound understanding of medicinal/organic chemistry to invent innovative medicines and advance the frontiers of drug discovery. Rohit has over 18+ years experience in discovery and optimization of small molecule drug candidates in both biotech and pharmaceutical industry. Rohit has a strong track record of success as a member of various cross functional project teams, contributing to the discovery and identification of multiple pre-clinical compounds and is listed as a co-inventor on over 12 patents. Rohit is a life learner and passionate about improving and extending people’s life by solving complex medical challenges through drug discovery.
Applications of machine learning for antibody discovery and optimization
- How is machine learning useful in the:
– antibody discovery process?
– antibody discovery process?
– antibody developability assessment?
- What does the future hold for machine learning in the antibody space?
Chief Scientific Officer, Twist Biopharma
Aaron is CSO of Twist Bioscience and leads our Twist Biopharma team. Prior to Twist, he served as Chief Scientific Officer of LakePharma, leading the California Antibody
Center, which discovers novel antibody therapeutics for its clients. He also oversaw all discovery research functions both as Vice President of Protein Sciences at Surrozen, and previously, as Vice President of Research at Sutro Biopharma, Inc. He also held director level positions at both Oncomed and Dyax Corp.
Data curation for property prediction in Drug Discovery
- Strategies for combining public and private data repositories of ADME properties
- Leveraging NLP & text mining for curating datasets from patents
- Best practice to follow for standardizing ADME property data
- Strategies for operating in low-data regime
Associate Director, Computational Chemistry, Nimbus Therapeutics
Leela Dodda is Associate Director, Computational Chemistry at Nimbus Therapeutics. At Nimbus, Leela is working on developing novel GNN models for predicting ADMET properties. In particular, he is interested in leveraging GNN’s ability to do multi-task learning and Transfer learning from Quantum Mechanical data for creating models that are not data-hungry. He got his Ph.D. in Computational Chemistry from Yale University, working on novel methodologies for structure-based drug discovery.
Before Nimbus, Leela was at VantAI, the machine learning-focused subsidiary of Roivant Sciences. At VantAI/Roivant, he helped set up the de novo degrader design engine and led the company’s degrader efforts in collaboration with Proteovant. Prior to this, Leela was part of the Computational Design Group at Silicon Therapeutics, supporting early discovery projects and developing the company’s De Novo Design workflows.
Poster Session: AI for drug discovery at X-Chem
- X-Chem’s AI services
- The application of ML to internal core activities
Research Scientist, Discovery Chemistry, XChem
Ryan Walsh is a research scientist on the discovery chemistry team at X-Chem. His background encompasses synthetic organic chemistry, cheminformatics, and machine learning, and he is passionate about leveraging the richness of DNA-encoded library (DEL) data to navigate therapeutically relevant
chemical space. He has designed, developed, and synthesized numerous libraries at X-Chem, and
coauthored a review article describing recent advances in the field of DEL reaction methodology
development. Ryan received his B.A. in chemistry and mathematics from the College of the Holy Cross, and his M.S. in chemistry from Northeastern University.