Speaker: Yaroslav Balytskyi, Physics and Astronomy, Wayne State University

Time: Wednesday, March 4, from 2:30 PM to 3:30 PM

Location for in-person participants: 1146 FAB 

Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1

Title: RAPID-Net: Accurate pocket identification for binding-site-agnostic docking

Abstract: Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. I will present RAPID-Net, a Deep Learning-based algorithm we developed for accurate prediction of binding pockets and seamless integration with docking pipelines. On the most challenging time split of PoseBusters, aiming to assess generalization ability (structures submitted after September 30, 2021), RAPID-Net-guided AutoDock Vina achieves 53.1% of Top-1 poses with RMSD < 2 Å and PB-valid, only six percentage points below 59.5% for AlphaFold 3. Crucially, RAPID-Net achieves this performance at much lower computational cost, enabling superior scalability for large-scale virtual screening campaigns. Unlike AlphaFold 3, RAPID-Net is not constrained by protein size and can be applied to very large macromolecular assemblies and efficiently process large ensembles of protein conformational states. Across diverse benchmark datasets, RAPID-Net outperforms other pocket prediction tools and accurately identifies distal functional sites, offering new opportunities for allosteric inhibitor design. In the case of the RNA-dependent RNA polymerase of SARS-CoV-2, RAPID-Net uncovers a wider array of potential binding pockets than existing predictors, which typically annotate only the orthosteric pocket and overlook secondary cavities.

Bio: Dr. Yaroslav Balytskyi is a computational physicist developing scientific software and modeling across molecular biophysics, quantum information, and particle physics. He defended his Ph.D. in Applied Physics in August 2023 at the University of Colorado Colorado Springs and is currently a postdoctoral researcher in the Department of Physics and Astronomy at Wayne State University. In addition to the recent development of RAPID-Net and its applications across diverse protein targets, which will be the focus of the upcoming talk, he recently proposed a theoretical model explaining statistically significant anomalies observed in rare meson decays. This theoretical framework is currently under experimental investigation at the Thomas Jefferson National Accelerator Facility Eta Factory program.

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AIDaS: CAD Seminar Series

Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)

The CAD Seminar Series is a primary seminar series at Wayne State University’s Institute for AI and Data Science (AIDaS). It is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.

Objectives:

• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.

• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.

• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.

Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.

Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.