Automation Workflow for High-Throughput Screening (HTS) Plate Design

Manual High-Throughput Screening (HTS) plate design is a critical operational bottleneck, consuming days of scientist time per campaign and introducing costly errors in compound concentration, control placement, and solvent compatibility. Automating this workflow directly accelerates R&D timelines by converting campaign parameters into optimized, executable plate maps and worklists in minutes. The business value stems from eliminating manual rework, improving assay quality through algorithmic optimization, and ensuring seamless handoff to robotic liquid handling systems like Hamilton or Tecan, which reduces setup errors and compound waste.

Implementation requires a central orchestrator, likely built with LangGraph, that sequences specialized agents for compound retrieval, solvent compatibility checks, and control positioning. It integrates with enterprise systems like a Dotmatics compound registry, a Titian Mosaic sample manager, and electronic lab notebooks (ELNs). Critical controls include a mandatory human review gate for final plate map approval, exception routing for edge cases, and full audit trails linking design decisions to physical execution. The architecture must handle data quality issues, such as missing compound concentrations, and provide rollback capabilities for entire campaigns.

This workflow automates a critical, error-prone bottleneck in drug discovery: designing assay plates for high-throughput screening (HTS). It replaces manual spreadsheet-based layout with an agentic system that ingests compound libraries, assay parameters, and liquid handler constraints to generate optimized plate maps. The operational upside comes from eliminating plate-layout errors that invalidate entire screening runs, reducing setup time from days to hours, and improving data quality through statistically sound control placement and solvent compatibility checks.

Implementation requires integrating the orchestrator with core lab systems: a compound registry (e.g., Dotmatics), a Laboratory Information Management System (LIMS), and liquid handler APIs (e.g., Hamilton, Tecan). The architecture uses LangGraph or a custom Python orchestrator to manage specialized agents for concentration normalization, edge-effect minimization, and worklist translation. Critical controls include a mandatory human review gate before execution, versioning for audit trails, and real-time validation against instrument deck layouts to prevent physical collisions.

The first phase establishes core orchestration and a 'human-in-the-loop' validation gate. We deploy a LangGraph-based orchestrator that ingests assay parameters and compound lists from your ELN or compound registry. It calls a specialized agent to generate an initial plate map, optimizing for controls, edge effects, and solvent compatibility. This map is not sent directly to the liquid handler; instead, it's pushed to a secure review interface for the lead scientist. This controlled launch validates the AI's logic against human expertise, builds team trust, and creates a gold-standard dataset for subsequent model refinement without disrupting live operations.

Upon validation, Phase 2 integrates the approved design into downstream execution. The orchestrator automatically formats the map into a vendor-specific worklist (e.g., for Hamilton, Tecan) and dispatches it via API to the liquid handling system's scheduler. Concurrently, it updates the LIMS with the final plate barcode and layout. We implement observability via a dashboard tracking design cycle time, approval rates, and exception reasons. This phased approach de-risks adoption, ensures assay integrity, and delivers measurable ROI through reduced manual design labor and faster campaign setup, typically cutting plate design time from hours to minutes.

Operationalizing HTS plate design automation requires a control architecture that enforces business rules, manages exceptions, and provides a clear audit trail. The workflow must validate inputs like compound concentration and solvent compatibility against assay specifications, flagging conflicts for human review. This prevents downstream assay failure and waste of expensive reagents. Governance is implemented through configurable rule engines integrated with compound management databases (e.g., Dotmatics, Benchling) and electronic lab notebooks (ELNs), ensuring every design decision is traceable to its source data and validation logic.

A phased rollout mitigates risk. Start with a pilot, automating plate design for a single, well-characterized assay and routing all outputs through a mandatory scientist review gate. This validates the AI's layout logic against historical performance. Subsequent phases gradually increase complexity—adding new assay types, integrating with more liquid handling platforms (Tecan, Agilent), and reducing the review gate to exceptions only. Continuous monitoring tracks key metrics like assay success rate and time-to-plate to quantify ROI, while version control on the rule engine allows for safe, iterative refinement of the automation logic.