Research

onepot CORE — an enumerated chemical space to streamline drug discovery, enabled by automated small molecule synthesis and AI

onepot team

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 used in medicinal chemistry, (ii) sourcing and curating building blocks from supplier catalogs, (iii) enumerating candidate products, and (iv) applying ML-based feasibility assessment to prioritize compounds for robust execution. In the current release, the space is supported by seven reactions.

We describe an end-to-end workflow — from route selection and automated liquid handling through workup and purification. We further report validation across operational metrics (success rate, timelines, purity, and identity), including NMR confirmation for a representative set of synthesized compounds and assay suitability demonstrated using a series of DPP4 inhibitors. Collectively, onepot CORE illustrates a path toward faster, more reliable access to diverse small molecules, supporting accelerated discovery in pharmaceuticals and beyond.

AI Agents in Drug Discovery

Srijit Seal, Dinh Long Huynh, Moudather Chelbi, Sara Khosravi, Ankur Kumar, Mattson Thieme, Isaac Wilks, Mark Davies, Jessica Mustali, Yannick Sun, Nick Edwards, Daniil Boiko, Andrei Tyrin, Douglas W. Selinger, Ayaan Parikh, Rahul Vijayan, Shoman Kasbekar, Dylan Reid, Andreas Bender, Ola Spjuth

Artificial intelligence (AI) agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act, and learn through complicated research workflows. Building on large language models (LLMs) coupled with perception, computation, action, and memory tools, these agentic AI systems could integrate diverse biomedical data, execute tasks, carry out experiments via robotic platforms, and iteratively refine hypotheses in closed loops. This work provides a conceptual and technical overview of agentic AI architectures, ranging from ReAct and Reflection to Supervisor and Swarm systems, and illustrates their applications across key stages of drug discovery, including literature synthesis, toxicity prediction, automated protocol generation, small-molecule synthesis, drug repurposing, and end-to-end decision-making.

Are we close to the AI revolution in small molecule synthesis? Enabling autonomous synthesis with AI agents and automation

onepot team, Onepot AI, Inc., South San Francisco, CA

We present an AI-enabled platform for multi-step autonomous synthesis of small molecules, combining automated hardware with AI agents that employ large language models for literature review, reaction planning, and experimental analysis. Case studies demonstrate ultrafast synthesis from a 2.7B chemical space and accelerated two-step synthesis of custom libraries.