Agentic Workflow for Synthesizability Scoring and Route Planning

This workflow automates the critical bottleneck where promising virtual screening hits fail due to impractical synthesis. It replaces manual, post-hoc feasibility checks with an integrated agent that scores every candidate for synthesizability and proposes viable routes. The business value is direct: it prevents wasted months of medicinal chemistry effort on unrealistic targets, accelerates the handoff to synthesis teams, and ensures your discovery pipeline is populated with molecules that can actually be made, protecting R&D investment and shortening project timelines.

Implementation integrates a LangGraph orchestrator to manage the scoring agent, which calls specialized tools like IBM RXN for retrosynthesis and queries internal inventory systems (e.g., SAP Ariba, Benchling) for reagent availability. The architecture includes approval gates for borderline scores and logs all decisions to an audit trail for model governance. By embedding this as a mandatory step in the discovery funnel, you create a defensible, automated filter that grounds AI-generated designs in the reality of your lab's capabilities and supply chain, turning synthesizability from a retrospective hurdle into a proactive design constraint.

This workflow directly addresses the costly bottleneck where promising virtual compounds fail in the lab due to impractical synthesis. It automates the repetitive work of manually assessing synthetic feasibility by orchestrating retrosynthesis AI, scoring pathways for complexity and cost, and checking reagent availability against inventory systems like SAP or custom ELNs. The operational upside comes from preventing wasted chemistry cycles, reducing lead optimization timelines by weeks, and improving resource allocation by front-loading synthesizability into candidate selection.

Implementation integrates specialized agents within a framework like LangGraph, connecting to chemistry databases (e.g., Reaxys, SciFinder) and internal inventory APIs. The orchestrator manages the sequence, handles exceptions like ambiguous routes, and logs all decisions for audit. Required controls include confidence thresholds for automated approval, mandatory human review for borderline scores, and integration with electronic lab notebooks for seamless handoff. This creates a production-grade system that operationalizes chemistry intelligence, turning a manual, expert-dependent process into a scalable, data-driven gate.

The workflow automates the critical bottleneck where promising virtual compounds fail due to impractical synthesis. It replaces manual, post-hoc feasibility checks with an integrated scoring agent that uses retrosynthesis AI (e.g., ASKCOS, IBM RXN) to propose pathways, then scores them for step count, reagent cost, and complexity. By providing synthesizability scores and draft routes alongside molecular designs, this prevents the selection of unrealistic targets, reduces wasted medicinal chemistry effort, and creates a defensible data trail for project governance and IP strategy.

Implementation begins by integrating the orchestrator with the compound registry and electronic lab notebook (ELN). Phase 1 establishes core scoring against commercial reagent catalogs. Phase 2 adds internal inventory checks via API to SAP Ariba or similar systems. Phase 3 introduces human-in-the-loop review gates for low-confidence routes. The architecture, built on LangGraph or a custom Python orchestrator, must manage API rate limits, cache results, and log all decisions for audit. This delivers immediate value by filtering out 20-30% of infeasible candidates before synthesis planning begins.

Operationalizing this workflow demands controls at each decision layer to prevent the propagation of unrealistic or high-risk synthesis plans. The orchestrator must enforce validation gates: checking reagent availability against live inventory (e.g., in a system like SAP S/4HANA or a custom ELN), applying cost and step-count thresholds, and routing low-confidence proposals for human chemist review. This prevents downstream waste in medicinal chemistry resources and grounds AI-driven design in practical laboratory economics, protecting project timelines and synthesis budgets.

A phased rollout is critical. Start by deploying the workflow in 'advisor mode' for a single project team, where it suggests routes but requires mandatory chemist sign-off. This builds trust and refines scoring logic with real feedback. Phase two automates scoring for internal compound libraries, integrating with inventory systems. The final phase enables fully autonomous scoring for early-stage virtual screening hits, with robust exception handling and monthly model performance reviews to detect drift in prediction accuracy against actual lab outcomes.

This workflow automates the costly bottleneck where promising virtual hits are deemed unsynthesizable, wasting computational and medicinal chemistry effort. An agentic system uses retrosynthesis AI to propose synthetic pathways, scores them for complexity, step count, and cost, and checks reagent availability against internal inventory systems like ELNs and procurement platforms. By providing synthesizability scores and draft routes alongside molecular designs, it prevents the selection of unrealistic targets, reduces late-stage attrition, and accelerates project timelines by weeks.

Implementation requires orchestrating specialized agents—one for retrosynthesis using tools like ASKCOS or IBM RXN, another for cost modeling, and a third for inventory checks—within a framework like LangGraph. The architecture must integrate with the compound registry and ELN (e.g., Benchling, Dotmatics) to log scores and proposed routes. Critical controls include human review gates for low-scoring molecules, confidence thresholds for automated routing, and continuous model validation against actual synthesis outcomes to refine predictions.