In silico cryptic binding site detection and prioritization
Date & Time - July 30th, 2025
- 9:00 AM PDT | 12:00 PM EDT | 5:00 PM BST | 6:00 PM CEST
Location - Virtual
Targeting cryptic binding sites is becoming an increasingly powerful strategy for tackling challenging drug targets, especially where traditional orthosteric approaches fall short due to issues like selectivity, resistance, or poor developability. However, identifying and evaluating cryptic binding sites—especially cryptic sites not visible in apo structures—remains a key challenge in early drug discovery.
In this webinar, we will introduce a novel computational workflow that integrates mixed solvent molecular dynamics (MxMD) with SiteMap to reveal and identify cryptic binding sites. This new combined workflow achieved a remarkable 83% success rate in detecting the cryptic binding sites within a retrospective benchmark set of 61 targets.
Join us to learn how this new workflow can support the identification of cryptic binding sites and enable more structure-based drug discovery campaigns for novel targets.
Webinar Highlights
- Overview of the MxMD method
- Introduction of new MxMD+SiteMap workflow to identify cryptic binding sites
- Benchmarking the new workflow against popular machine learning methods and SiteMap in its default pocket detection mode
Our Speakers
Da Shi
Principal Scientist I, Life Science Software, Schrödinger
Da Shi is a Principal Scientist in the Hit Discovery team at Schrödinger. He obtained his Ph.D. at the University of California San Diego with the supervision of Prof. Ruben Abagyan. After graduation, he worked at the Frederick National Laboratory for Cancer Research as a Data Scientist on developing machine learning platforms for drug discovery. In 2021, he joined Schrödinger and worked as an All Access Applications Scientist. He later transitioned to the Hit Discovery team working on developing workflows on cryptic binding site identification and FEP ligand pose generation.
Dima Lupyan
Senior Principal Scientist, Life Science Software, Schrödinger
Dr. Dmitry Lupyan, a product manager, spearheads the development of Desmond and FEP analysis tools, showcasing his expertise in the realm of molecular dynamics. Notably, he’s behind the Python API for simulation analysis, a cornerstone utilized across Schrödinger’s MD, MxMD, and FEP+ products. Driven by a passion for scientific advancement, he actively promotes the utilization of simulation analysis tools, fostering a community of exploration. His research interests delve into the intricate domains of protein engineering, membrane-bound systems, and the fascinating dynamics of unbinding kinetics.