Advancing Drug Development with AI and Computational Science

The Frederick National Laboratory for Cancer Research together with the Frederick County Chamber of Commerce organizes the quarterly Biotech Connector Speaker Series. This event promotes and supports the Frederick County and surrounding areas’ biotech and bioscience community and provides an inside look at local advances.

Please join fellow biotech and bioscience professionals for our first event in 2026. The speakers for the event are:

• Trent Balius, Ph.D., computational scientist, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research
• Abhishek Kognole, Ph.D., scientific & business development manager, SilcsBio LLC
• X. Simon Wang, Ph.D., associate professor of medicinal chemistry, Howard University College of Pharmacy

Presentations

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Traditional Molecular Docking Tools: Still Relevant for Drug Discovery in the Age of Machine Learning

Abstract

In this talk, I will briefly discuss the RAS Computational Chemistry Research Team. Our main tool is molecular docking, but we also use chemical informatics, molecular dynamics with free energy methods, and some machine learning to support our docking. The main use of molecular docking is as a drug discovery tool—and we are applying this to study RAS proteins that are implicated in over 20% of all cancers. I will discuss the work we are doing in DOCK method development. DOCK is the first docking software and has been continuously developed since the 1980s. I will discuss porting a method into DOCK 6 to enable large-scale docking and describe a covalent docking algorithm. I will touch on the impact of machine learning on the docking community and opportunities for DOCK to continue to be relevant in this new world where machine learning is a force.

Bio

Trent Balius is a Computational Scientist at the Frederick National Laboratory for Cancer Research and part of the NCI RAS Initiative. In his research, he develops and uses computational methods to improve therapeutics focusing on RAS cancer targets. Trent did his postdoctoral work with Brian Shoichet at the University of California, San Francisco (UCSF) and for a year at University of Toronto. In 2012, he received his PhD from Stony Brook University with Robert Rizzo. Trent graduated from the University of Pittsburgh at Greensburg with a B.S. in Applied Mathematics in 2006. Trent grew up in Frederick County.

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Overcoming the Trade-off Between Speed and Accuracy in Computer Aided Drug Design

Abstract

CADD in an industrial setting requires high accuracy with maximum throughput to drive the drug discovery process. The Site Identification by Ligand Competitive Saturation (SILCS) SILCS technology, in conjunction with the CGenFF program, attains this through the pre-computed FragMaps that can be used throughout the drug discovery and optimization process. SILCS FragMaps offer the ability to identify novel allosteric sites, perform virtual screening through both pharmacophore and SILCS docking approaches, rapidly estimate relative ligand binding affinities without requirement of an experimental structure of the lead compound, extract atomic detail contributions to ligand binding and iteratively improve the predictability of the FragMaps during the optimization process. Beyond small molecule drug development, the comprehensive nature of the SILCS FragMaps allows for analysis of protein-protein interactions that may be combined with docking of excipients, buffers and ions to the full protein surface to guide the formulation of protein-based biologics including monoclonal antibodies.

Bio

Bachelor of Chemical Engineering from University of Mumbai, PhD in Chemical Engineering from University of Kentucky with a focus on applying computational approaches to learning the protein-ligand interactions. Postdoc from University of Maryland, Baltimore with focus on Computer-aided drug design. Dr. Kognole brings molecular modeling, enhanced sampling, and forcefield development experience to the SilcsBio team. He is passionate about integrating computational approaches with experiments for faster and better drug design.

An interesting fact about the speaker: During graduate school, Jon had a business installing (and removing) lofts in the freshman dormitories. 

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AIDD – How AI Is Reshaping Biopharma

Abstract

Dr. Wang will highlight how AI is transforming drug discovery. Artificial Intelligence for drug discovery (AIDD), the evolution of computer-aided drug design, is now involved in all stages of development pipeline from target identification to market, slashing timelines and costs. Preclinical phases can be halved, phase I accelerated. Pharma giants are partnering with AI companies, applying it in all key therapeutic areas. AI shines in small molecule drugs, but its reach is expanding to large molecule drugs like antibodies and ADC. In recent years, Dr. Wang’s lab has pioneered the application of AI/ML methodologies including LLMs to expedite early drug development, achieving successes with multiple targets. He has also championed the use of AI/ML to fine-tune reaction conditions for visible-light photocatalysis in heterocycle synthesis. Recent studies on PEG chain length and rational linker design for ADCs will also be highlighted.

Bio

Dr. Simon Wang is a tenured professor at Howard University and Director of the AI & Drug Discovery Core for the DC CFAR. With 25+ years in AI-driven drug discovery, computational biomedicine, and biomolecular simulation, he advances practical applications of AI for therapeutic innovation. He has authored ~100 scholarly works and delivered 250+ presentations and invited talks worldwide. Dr. Wang also serves as a reviewer for 50+ journals and on NIH, NSF, and NASA panels, and has consulted for the Federal Reserve Board and evaluated projects for the Regeneron Science Talent Search.