Frontier Systems
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AI Retrosynthesis Yield Coverage
Agentic AI-Driven Chemical Manufacturing
What fraction of steps in routes to three API intermediates has condition-resolved yield support, against a 70% overall yield bar in four steps or fewer?
THE QUESTION
Against round-trip retrosynthesis accuracy of 89.5% top-1 on USPTO-50K, what fraction of AI-selected routes for a named API intermediate reach 70% or more overall yield in four steps or fewer when physically executed on an automated platform — and does executed yield land within 15% of prediction?
WORK ORDER
Coverage of condition-resolved yield support across proposed four-step routes to three API intermediates
BACKGROUND
What this Apodex Lab is
Manufacturing active pharmaceutical ingredients requires multi-step chemical syntheses where material is lost at every transformation. Industrial process development often demands an overall yield of 70% or more across four steps or fewer. Automated retrosynthesis tools propose chemical routes to reach these target intermediates, but calculating an overall route yield by multiplying per-step estimates assumes each step has a reliable numerical yield under specific conditions. When executed chemical yields deviate by more than 15 percentage points from predictions, scale-up programmes stall and planned manufacturing schedules fail.
Current confidence in automated retrosynthesis rests on benchmarks like the 50,016 reactions of USPTO-50K, where models report round-trip plausibility figures such as 89.5% top-1 and 99.2% top-5 accuracy. This round-trip check assesses only whether a forward model considers a disconnection chemically feasible; it contains no reaction conditions, no solvent or ligand inputs, and no quantitative yield. Condition-resolved numerical yield prediction exists only for narrow reaction families with dense experimental data, such as iridium-catalysed aromatic C-H borylation and one-pot multicomponent networks with a squared correlation of about 0.81 across 20 labelled reactions. Across AI-generated multi-step routes, no study has measured what fraction of proposed steps falls within these model-supported families, leaving the empirical basis of multiplied route yields unverified.
The Agentic AI-Driven Chemical Manufacturing Apodex Lab audits four-step route graphs for three active pharmaceutical ingredient intermediates to establish this coverage baseline. Working from frozen taxonomies and blinding protocols, independent annotators classify each reaction step into defined support tiers across both plausibility-ranked and manufacturability-ranked routes, measuring the share of condition-supported steps with a 95% bootstrap interval half-width of 12 percentage points or better. If condition-resolved coverage is 15% or lower, process chemists stop inserting speculative overall-yield targets into binding development contracts, redirecting experimental resources toward collecting high-throughput yield data for the unsupported reaction families that dominate the proposed routes.
WHY NOW
Why the field is spending on an assumption
Retrosynthesis planners rely on round-trip plausibility checks, such as an 89.5% top-1 score across 50,016 benchmark reactions, to confirm proposed disconnections. These checks record whether a forward model predicts a reaction can proceed, but they supply no reaction conditions and no numerical yield. Quantitative, condition-resolved yield estimates exist only for narrow reaction classes, including iridium-catalysed aromatic C–H borylation and one-pot multicomponent reactions with a squared correlation of about 0.81 against 20 labelled reactions. Statements of work now demand a 70% overall yield across four steps or fewer within a ±15 percentage-point band. Assessing whether that specification is definable requires measuring what fraction of proposed route steps falls within reaction families that possess validated yield models.
WHAT WE’D BUY
What we would ask a laboratory to run
We are not buying the answer. We are buying a measurement with an interval — and the bars it will be compared against.
This work order classifies individual reaction steps from proposed four-step synthetic routes to three active-pharmaceutical-ingredient intermediates without performing physical chemistry. Annotators assign each step to one of three support tiers, establishing whether a condition-resolved quantitative yield estimate exists that accounts for substrate, ligand, and solvent. The measurement returns the percentage of steps supported by condition-resolved models for routes chosen by round-trip plausibility versus an expert process-development panel. Each estimate includes a 95% route-clustered bootstrap interval with a half-width of 12 percentage points or better. It also delivers the paired between-rule difference, the permutation null spread across at least 5,000 reassignments, annotator agreement clearing a 0.60 Fleiss kappa, and the share of overall multiplied yield supported by condition-resolved steps.
SCOPE & LIMITS
Because the measurement evaluates proposed route graphs across three intermediates without running physical reactions, it establishes the extent of model coverage rather than experimental validation of any predicted chemical yield.
WHO EXECUTES
The kind of lab that can run this
An independent team of process-chemistry experts and computational-chemistry reimplementers — for example a synthetic-route audit group at a contract process-development organisation working with an external cheminformatics team — who will re-annotate proposed route graphs to a named API intermediate by reaction family and independently rebuild the coverage accounting.
EXECUTOR KIND
independent-reimplementation
WAYS IN
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