onepot CORE -- an enumerated chemical space to streamline drug discovery, enabled by automated small molecule synthesis and AI
Andrei S. Tyrin, Brandon Wang, Manuel Mu\~noz, Samuel H. Foxman, Daniil A. Boiko

TL;DR
onepot CORE introduces an automated, AI-driven platform that enumerates and synthesizes 3.4 billion drug-like molecules, significantly streamlining early-stage drug discovery by reducing synthesis bottlenecks.
Contribution
It presents the first integrated system combining enumerated chemical space, automated synthesis, and AI planning to rapidly generate and validate diverse small molecules.
Findings
High success rate in automated synthesis workflows
Rapid validation of compounds via NMR and bioassays
Demonstrated potential for accelerated drug discovery processes
Abstract
The design-make-test-analyze cycle in early-stage drug discovery remains constrained primarily by the "make" step: small-molecule synthesis is slow, costly, and difficult to scale or automate across diverse chemotypes. Enumerated chemical spaces aim to reduce this bottleneck by predefining synthesizable regions of chemical space from available building blocks and reliable reactions, yet existing commercial spaces are still limited by long turnaround times, narrow reaction scope, and substantial manual decision-making in route selection and execution. Here we present the first version of onepot CORE, an enumerated chemical space containing 3.4B molecules and corresponding on-demand synthesis product enabled by an automated synthesis platform and an AI chemist, Phil, that designs, executes, and analyzes experiments. onepot CORE is constructed by (i) selecting a reaction set commonly…
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Taxonomy
TopicsComputational Drug Discovery Methods · Machine Learning in Materials Science · Chemical Synthesis and Analysis
