On June 1, 2026 GT Biopharma, Inc. (the "Company") (NASDAQ: GTBP), a clinical stage immuno-oncology company focused on developing innovative therapeutics based on the Company’s proprietary natural killer (NK) cell engager TriKE platform, reported an update on its newly implemented AI-based technological initiatives and improved pipeline discovery efficiencies, which are expected to lead to additional development candidates advancing into pre-IND development in 2027.
Schedule your 30 min Free 1stOncology Demo!
Discover why more than 1,500 members use 1stOncology™ to excel in:
Early/Late Stage Pipeline Development - Target Scouting - Clinical Biomarkers - Indication Selection & Expansion - BD&L Contacts - Conference Reports - Combinatorial Drug Settings - Companion Diagnostics - Drug Repositioning - First-in-class Analysis - Competitive Analysis - Deals & Licensing
Schedule Your 30 min Free Demo!
"We have seen a marked acceleration in our discovery productivity following recent initiatives implementing AI-based technologies, which have been adapted to improve our drug engineering capabilities," said Michael Breen, Executive Chairman and Chief Executive Officer. "As we continue to demonstrate clinical execution acumen with GTB-3650 and GTB-5550 advancing through Phase 1 this year, we are now looking forward to our next-generation assets with potential for shorter development timeliness, increased probability of clinical success, and lower development costs in the coming years."
Implementation of AI-based technology for GT Biopharma’s Discovery Pipeline
AI-guided sequence and structural analyses are used to identify de novo candidate tumor-targeting engagers and multi-domain proteins with favorable binding, stability, and developability profiles, enabling early prioritization of molecules most likely to demonstrate translation success beyond discovery.
These tools further inform rational engineering by optimizing domain orientation, linker design, and spatial architecture to enhance binding, support productive immune synapse formation, and minimize structural liabilities that can impair potency, manufacturability, or consistency.
In downstream applications, AI-based structural modeling is applied to predict surface exposure, steric compatibility, and assay performance, guiding construct refinement prior to resource-intensive in vitro and in vivo studies.
(Press release, GT Biopharma, JUN 1, 2026, View Source [SID1234666350])