Evolving drug discovery using AI, automation, and ASMS through an integrated D-preMTA-MTA strategy for target-focused library exploration

Abstract

Abstract In the realm of drug discovery, the Design-Make-Test-Analyses (DMTA) cycle plays a pivotal role, with the “M” phase often emerging as a bottleneck. Numerous endeavors have sought to expedite this phase, employing technologies like robotic arms, flow chemistry, and other devices for automated synthesis. This paper introduces “preMTA” as a game-changer, leveraging artificial intelligence (AI)-based molecular generative models, automated synthesis workflows, and the ASMS platform, creating the “D-preMTA-MTA” cycle. During the “preMTA” phase, AI-designed chemical spaces for specific targets are explored. Microgram-scaled target compounds are efficiently prepared through pooling reactions and streamlined work-ups via automated synthesis workflows. These compounds are then screened using ASMS to rank them based on competitive binding affinity. This strategic approach enables the subsequent MTA phase to focus on potent binders. Validation of this approach involved the discovery of novel inhibitors for T-cell protein tyrosine phosphatase (TCPTP or PTPN2). In the “D” phase, a target-focused library of 696 compounds was designed, with 140 strong binders swiftly identified in the “preMTA” phase. Subsequently, 51 of these compounds were scale-up synthesized and confirmed with IC50 values ranging from 16 nM to 277 nM, with 17 exhibiting IC50 values below 50 nM. The efficacy and seamless integration of each phase in the “D-preMTA-MTA” cycle can be attributed to a) the use of a synthesis-oriented molecular generative method streamlining automated synthesis, b) the deployment of a versatile and adaptable robotic arm capable of multitasking, and c) the implementation of ASMS screening methods reducing both synthetic and bio-testing complexities.