Technology Benchmarks
SynovAI's retrosynthetic platform turns traditional inefficiencies into streamlined, enriched, and collaborative synthetic design solutions.
| Traditional Retrosynthesis | Benchmark | SynovAI Retrosynthesis |
|---|---|---|
Familiar chemistry | Opportunities | Data-backed alternative routes |
Up to a handful | Full Routes Generated | Over a hundred |
Slow ideation, drawing, data share, and feedback cycle | Collaboration | Auto-generated and linkable workspace |
Manually via many tools | Multi-criteria Optimization | Integrated in a single tool |
Hours–days | Route Generation Efficiency | Seconds–minutes |
Multi-objective scoring
Generating routes is the easy part. Knowing which one to run is the hard part.
SynovAI scores every route across the dimensions that decide whether it's real, then ranks them in one view your whole team works from.
Feasibility & confidence
Every route carries a model confidence signal, so chemists triage the most viable paths first instead of reading hundreds of options blind.
Cost & scalability
Routes are weighed on step efficiency, projected cost, and how well they hold up when the chemistry moves from milligrams to kilograms.
Sourcing, built in
Each intermediate is checked against building-block and vendor availability, so a promising route isn't a dead end at procurement.
IP & freedom-to-operate
Routes carry a patent signal and map against your launch timeline, so commercialization risk is visible before you commit, not after.
Why SynovAI
Three things that do not usually come together
Most retrosynthesis tools are a search over hand-written rules, a literature lookup, or a model with no route to a price. These are the parts of SynovAI that are structural rather than incremental.
No hand-coded reaction rules
The model is learned end to end from reaction data rather than assembled from a template library, so it proposes disconnections that no rule set contains.
DirectMultiStep · J. Chem. Inf. Model. 2025
Route length does not degrade the answer
Complete multi-step routes are generated directly instead of being assembled one step at a time by tree search, so performance holds as a synthesis gets longer rather than thinning out with depth.
DirectMultiStep · J. Chem. Inf. Model. 2025
It costs routes at your prices, not list
Enterprise teams load their own contracted pricing, and every costed route reports which parameter set produced the number. A catalog aggregator can only ever quote list.
Platform capability
And the things a working chemist needs anyway
Routes that were run, not just ranked
Pathways proposed by the platform have been taken to the bench and the products characterized, in peer-reviewed work.
Novel structures accepted
Not limited to compounds that already exist. Targets that have never been made get full routes.
One workspace, four specialisms
Medicinal chemistry, process, IP and procurement read the same linkable route and sign off on it together.
Seconds to minutes
A target entered at the start of a meeting has a ranked route set before the meeting ends.
Research
Publications
Site-Specific Template Generative Approach for Retrosynthetic Planning
Yu Shee, Haote Li, Pengpeng Zhang, Andrea M. Nikolic, Wenxin Lu, H. Ray Kelly, Vidhyadhar Manee, Sanil Sreekumar, Frederic G. Buono, Jinhua J. Song, Timothy R. Newhouse & Victor S. Batista
Nature Communications · 2024
Introduces the generative approach that learns where a molecule should be disconnected, the foundation for how the platform proposes realistic routes.
DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis
Yu Shee, Anton Morgunov, Haote Li & Victor S. Batista
Journal of Chemical Information and Modeling · 2025
Generates complete multi-step routes directly instead of searching one step at a time, which is what lets the platform return many full routes per target.
Collective intelligence for AI-assisted chemical synthesis
Haote Li, Sumon Sarkar, Wenxin Lu, Patrick O. Loftus, Tianyin Qiu, Yu Shee, Abbigayle E. Cuomo, John-Paul Webster, H. Ray Kelly, Vidhyadhar Manee, Sanil Sreekumar, Frederic G. Buono, Robert H. Crabtree, Timothy R. Newhouse & Victor S. Batista
Nature · 2026
Coordinates specialized models to plan and experimentally realize de novo synthesis, linking prediction to chemistry validated at the bench.
71% first-attempt success
Across 35+ novel compounds, following experimental procedures proposed by MOSAIC. MOSAIC is the condition-prediction model behind HowSyn, which is in beta.
Intellectual Property
Built on protected, published science
Our platform is grounded in peer-reviewed research and licensed methods. Our intellectual property spans the core of how routes are generated, scored, and made real.
Generative route design
Models that generate complete multi-step routes directly, without hand-coded reaction templates.
Multi-objective route scoring
Ranking routes across feasibility, cost, scalability, and sourcing in a single pass.
Synthesis condition prediction
Methods for predicting the reagents and conditions that turn a proposed route into one a bench can run.
Put SynovAI on your next molecule
Bring a structure to a working session with our team, or start in the platform today.