<figure><img src="image_1.png" /></figure>

HAL
open science

# RetroRules 2026: multi-dataset combining chemistry and biochemistry for reaction templates

Thomas Duigou, Philippe Meyer, Jean-Loup Faulon

► To cite this version:

Thomas Duigou, Philippe Meyer, Jean-Loup Faulon. RetroRules 2026: multi-dataset combining chemistry and biochemistry for reaction templates. 2025. hal-05267743

HAL Id: hal-05267743

https://hal.science/hal-05267743v1

Preprint submitted on 18 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# RetroRules 2026: multi-dataset combining chemistry and biochemistry for reaction templates

Thomas Duigou¹*, Philippe Meyer¹, Jean-Loup Faulon¹,²

1: Université Paris-Saclay, AgroParisTech, INRAE, Micalis Institute, 78350, Jouy-en-Josas, France

2: The University of Manchester, Manchester Institute of Biotechnology, Manchester M1 7DN, U.K

*: corresponding author

## Abstract

RetroRules (https://retrorules.org) is a unique compendium of reaction templates, which are generic abstractions of biochemical transformations, supporting pathway discovery, reaction prediction and enzyme engineering. The 2026 release updates biochemical sources (MetaNetX, Rhea), newly integrates chemical sources (USPTO), expands template radii to 0–10 to control specificity, adopts implicit-hydrogen and minimal atom primitive encoding for faster, more compact rules, and enables templates from mass-imbalanced reactions increasing coverage of biochemically relevant entries. Atom–atom mapping transitions to the transformer-based rxn mapping, improving mapping accuracy. The RetroRules 2026 release comprises 1,062,364 templates from 88,425 curated source reactions, with coverage across 7,391 fourth-level EC classes. A redesigned website, an updated Online Template Generator, and an OpenAPI-defined API enable multi-criteria exploration (dataset/radius/EC), deduplicated template views, and convenient exports (dataset-specific TSV/CSV/JSON bundles). Sequence annotations are refreshed from UniProt and summarized as a normalized sequence-support score to guide ranking. Together, these RetroRules evolutions provide a cross-domain resource bridging biochemistry and organic chemistry with broader coverage, controllable specificity, and better usability for high-throughput pathway design, reaction prediction and enzyme engineering.

**Keywords:** reaction templates; rule-based reaction; retrobiosynthesis; metabolic engineering; SMARTS; Enzyme classification; MetaNetX; Rhea; USPTO.
## Graphical Abstract

<figure><img src="image_3.png" /></figure>

**Caption:** RetroRules integrates biochemical and chemical sources to produce sequence-aware reaction templates across multiple radius. Templates are linked to EC classes, source reactions, and UniProt accessions, and are accessible through an updated web interface and REST API.

## Introduction

Reaction templates[1] abstract chemical reactions into reusable patterns that capture changes in atoms and bonds (the reaction centre). They also include a defined local environment (often parameterized by a radius around the reaction centre) to tune specificity in line with enzyme-relevant substrate recognition. By decoupling mechanism from specific instances, templates support pathway discovery, reaction prediction, and the systematic evaluation of alternative biochemical routes. Template-based strategies are extensively used in computer-aided retrosynthesis and retro-biosynthesis tools, alongside template-free approaches, and both are increasingly integrated with machine learning[2-4].
Since 2019, RetroRules[5] has provided such templates and, as a unique resource, offers multi-radius variants alongside sequence-aware scoring and programmatic access. It has underpinned multiple computational design pipelines[6-9], analytical tools[10-14] and metabolic engineering studies[15-18].

In the years following the original release, both the biochemical curation landscape and cheminformatics tooling advanced. On the curation side, databases such as MetaNetX[19] and Rhea[20] expanded and refined coverage, while UniProt[21] continued to consolidate protein evidence and cross-references. On the tooling side, transformer-based atom-atom mapping (AAM) improved mapping accuracy and robustness[22,23]. In parallel, expectations grew for usable web interfaces[24,25], stable APIs, and reproducible distribution formats. Furthermore, combining biochemical and organic reaction knowledge further enlarges the design space and enables hybrid routes that mix enzymatic and synthetic steps, a direction supported by recent studies showing gains in pathway prediction and chemical planning[26,27].

Here we present a substantial update of RetroRules that addresses four core objectives:

1. Multi-dataset integration. Templates and reactions from biochemical sources (MetaNetX, Rhea) and a large corpus of organic chemistry reactions (USPTO) are integrated with preserved provenance. This enables the use of extended collections of templates mixing both biochemistry and chemistry domains from a unique one-stop shop, which may broaden biosynthetic and biodegradation possibilities.

2. Expanded and refined template encoding. Radius range extends to 0-10 (including radius 0 for reaction-centre-only patterns and 9-10 for higher specificity), and template encoding adopts implicit hydrogen to align with common cheminformatics toolkits and accelerate rule application. Templates are also derived from mass-imbalanced reactions, increasing the coverage of new enzymatic entries that were not strictly balanced in source databases.

3. Improved mapping and sequence evidence. Atom-atom mapping is now based on rxnmapper[22], improving accuracy over earlier RDT approach[23]. Sequence evidence is refreshed against recent UniProt releases and summarized as a normalized sequence-support score in [0,1] to facilitate ranking.

4. Usability, performance, and access. A redesigned website now supports multi-criteria filtering (dataset, radius, EC number, reaction ID), provides deduplicated provenance-aware views, and stable exports. A reworked REST API mirrors these new capabilities. Archival releases remain available for reproducibility.
# Results

## Data sources and scope

**Biochemical datasets.** Biochemical reactions were updated from MetaNetX[19] (v4.4) and Rhea[20] (release 139), both offering transformations with cross-references to compounds (e.g., ChEBI[28]) and EC assignments. Original reaction identifiers and links are retained for traceability. Sequence evidence is updated via Rhea and MetaNetX cross-references to UniProt; accessions are associated with biochemical reactions and propagated to derived templates.

**Chemical dataset.** To complement biochemistry, the USPTO-50k[29] corpus is added, a curated subset of reactions extracted from US patents (1976-2016) based on Lowe's work[30], extensively used as a benchmark in computational chemistry and machine learning for synthesis planning. It contributes a large and diverse set of organic transformations, extending functional-groups and context coverage beyond enzyme-reported reactions. USPTO entries do not contribute sequence accessions.

**Identity and provenance.** Each canonical template is defined by its canonical SMARTS pattern and generated per dataset, linked back to its source reaction(s), substrates/products, and (when available) EC annotations and UniProt accessions. In the web interface, deduplicated views merge identical templates across datasets while preserving per-dataset evidence and tagging their origin as “biochemical” or “organic chemistry”. This scope unites enzyme-catalysed biochemistry with broader organic reactivity while maintaining provenance for downstream analysis.

## Template extraction pipeline

Template generation follows the RetroRules 2019 pipeline: curated reactions are extracted and filtered to remove non-transforming or underspecified entries, the reaction centre is located by atom-atom mapping, multi-substrate reactions are decomposed into mono-substrate components in both directions with product atoms required to inherit from the focal substrate, optional cofactor pruning is applied, and SMARTS templates are emitted at multiple radii around the centre. Detailed methods appear in the original RetroRules paper[5] and in the online documentation at https://retrorules.org/docs.

Relative to 2019, explicit-hydrogen syntax is no longer used, reducing representation variance and speeding downstream use[31], and structural normalization now includes stereochemistry stripping before template generation. Atom-atom mapping migrated from the MCS-based Reaction Decoder Tool[32] (RDT) to the transformer-based rxn mapping (v0.4.2)[22], with reported improved accuracy of 83.74% for rxn mapping versus 76.23% for RDT[23]. Cofactor pruning uses a curated list for biochemical
sources[5] and the Schneider et al.[29] list for USPTO, both cofactor lists are provided in the Supplementary Data. Mass-imbalanced reactions are now admitted, expanding enzymatic coverage. Each candidate template must pass a round-trip quality check: applying the template back to the source substrate must regenerate the expected product(s).

## Template encoding

Templates are generated as SMARTS[1] at radii 0–10 (previously 1–8), enabling both highly general (radius 0) and highly specific (9–10) contexts. The encoding adopts implicit hydrogens, aligning with major cheminformatics toolkits default options and yielding smaller pattern size (~1.7× smaller on average) and substantially faster application (5–10× empirically) by reducing graph size and canonicalization overhead. Additionally, the set of atomic primitives is kept minimal: atom symbol, aromatic/aliphatic status and atomic number, as well as charges and hydrogens only when required to preserve structural consistency. Combined with the canonical SMARTS generation (RDKit-based procedures adapted to SMARTS), this minimizes representation variance and promotes merging of previously near-duplicate patterns into unique canonical forms. Each template instance retains dataset labels (MetaNetX, Rhea, USPTO) and all provenance, while the user interface offers deduplicated views that aggregate evidence across datasets without losing the per-dataset breakdown.

## Handling mass-imbalanced reactions

Upstream biochemical resources (MetaNetX v4.4, Rhea rel. 139) include biologically valid reactions with incomplete or generic stoichiometry (e.g., implicit protons/water, lumped cofactors, class placeholders). Earlier release excluded such entries, limiting coverage and discarding transformations with clear mechanistic cores. The 2026 update now admits mass-imbalanced reactions when the atom-atom mapping could be successfully performed. This increases coverage of biochemically relevant reactions and contributes to the expanded level-4 EC span (Table 1).

## Release content and coverage

**Global volumes, per-dataset counts.** The 2026 release contains 1,062,364 canonical templates derived from 88,425 source reactions: 26,793 from MetaNetX, 11,636 from Rhea, and 49,996 from USPTO. By dataset, templates total 243,761 (MetaNetX), 117,859 (Rhea) and 758,802 (USPTO). Compared with 2019 (168,441 templates, 16,789 source reactions), this represents a near six-fold expansion, driven by the inclusion of USPTO and broader coverage in Rhea/MetaNetX. Table 1 provides a detailed summary of counts.

**Dataset overlaps.** Overlap analysis are shown in Figure 1. The sizable MetaNetX–Rhea intersection (57,798 templates) reflects convergent biochemical curation, which is
expected considering MetaNetX proposes a reconciliation of chemical namespace
between multiple biochemical databases including Rhea, whereas overlap with USPTO
remain minimal underscoring domain differences (~1,500 templates). The datasets
therefore act complementarily, with USPTO adding organic-chemistry patterns that
extend the biochemical space.

**Per-radius distribution.** Templates are encoded at radii 0 to 10 around the reacting atoms. As shown by Figure 2A, per-radius totals increase and then plateau with context size (e.g., 51,231 at radius 0, 165,612 at radius 3, 191,796 at radius 10 shows). Figure 2B reports the marginal contribution of each radius, defined as templates first observed at that radius, which rises steeply up to radius 3 then tapers toward radius 10.

**EC coverage.** Across biochemical sources (MetaNetX, Rhea), the release spans 306 third-level EC classes and 7,391 fourth-level EC classes which relates an increase of x1,4 and 1,7 compared with 2019 release (see Table 1; 2019: 219 ECs level-3; 4,374 ECs level-4). Table 1 shows partitions across the top-level EC. Inclusion of well-mapped, mass-imbalanced reactions (Section 3.4) and availability contribute to the broader coverage of the biochemical reactional space.
<table><caption>**Table 1.** Content of RetroRules from 2019 to 2026. The numbers report per dataset count (MetaNetX, Rhea, USPTO) and the Total corresponding to the union. The number of templates annotated with at least one UniProt sequence and the coverage in EC classes from top to fourth levels. A dash indicates the dataset was not included in the 2019 release.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Dataset</th><th colspan="2">Release</th><th rowspan="2">Evolution</th></tr><tr><th>2019</th><th>2026</th></tr></thead><tbody><tr><td rowspan="4">Source reactions</td><td>MetaNetX</td><td>16,786</td><td>26,793</td><td>x 1,6</td></tr><tr><td>Rhea</td><td>-</td><td>11,636</td><td>-</td></tr><tr><td>USPTO</td><td>-</td><td>49,996</td><td>-</td></tr><tr><td><strong>Total</strong></td><td>16,789</td><td>88,425</td><td>x 5,3</td></tr><tr><td rowspan="4">Templates</td><td>MetaNetX</td><td>168,441</td><td>243,761</td><td>x 1,4</td></tr><tr><td>Rhea</td><td>-</td><td>117,859</td><td>-</td></tr><tr><td>USPTO</td><td>-</td><td>758,802</td><td>-</td></tr><tr><td><strong>Total</strong></td><td>168,441</td><td>1,062,364</td><td>x 6,3</td></tr><tr><td>Templates with ≥1 UniProt</td><td><strong>Total</strong></td><td>134,599</td><td>152,552</td><td>x 1,1</td></tr><tr><td>EC level-1</td><td><strong>Total</strong></td><td>6</td><td>7</td><td></td></tr><tr><td>EC level-2</td><td><strong>Total</strong></td><td>60</td><td>77</td><td>x 1,3</td></tr><tr><td>EC level-3</td><td><strong>Total</strong></td><td>219</td><td>306</td><td>x 1,4</td></tr><tr><td>EC level-4</td><td><strong>Total</strong></td><td>4,374</td><td>7,391</td><td>x 1,7</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Upset graph of overlaps between MNX, Rhea, and USPTO. Top bars show the size of each intersection. The dot-line matrix below encodes set membership.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Unique templates count. **A.** Per radius unique template count. **B.** Contribution of each radius to the unique templates count. Stacked bars show the number of distinct templates that is first generated at the indicated radius within each dataset.</figcaption></figure>
## Sequence evidence and scoring

Sequence annotations derive from biochemical sources (Rhea/MetaNetX); USPTO contributes none. The number of templates with at least one UniProt accession slightly decreases by from 2019 (134,599) to the 2026 release (125,064) as reported in Table 1, reflecting upstream curation changes and deduplication. To provide simplified ranking key, the previous sequence evidence score[5] is normalized such that $s \in [0,1]$. Higher $s$ indicates denser, less ambiguous sequence support and remains comparable across datasets and radii. The score is exposed in the UI and API, enables sequence-aware prioritization in pathway search, and combines readily with other criteria (e.g., chemical similarity or EC agreement) without retuning. Additionally, templates derived from spontaneous reactions are no longer penalized for missing sequence evidence; they receive the best score of 1 rather than being down weighted.

## Web application and Online Template Generator

The website has been fully redesigned for navigation, search, and template exports, in addition to a responsive layout for desktop, tablet and mobile use. Template Explorer now supports multi-criteria search across datasets (MetaNetX, Rhea, USPTO), radius (0–10), EC number (including partial levels such as “2.7.1”), and reaction ID, with customizable sorting. Results are deduplicated by canonical SMARTS and displayed as compact cards with summary chips for key attributes such as radius range, EC count, related reaction count, sequence support, and dataset provenance. Each hit renders the SMARTS pattern and provides quick actions to copy the SMARTS, copy the template ID, or open a permalink. The annotated screenshot in Figure 3 illustrates the new layout and result summaries.

The Template Detail View aggregates evidence across datasets while preserving per-dataset source and links. Pages list source reactions, radius coverage, EC assignments (with cross-links to Brenda[33], linked compounds (with link-outs to MetaNetX and ChEBI), and associated UniProt accessions when available. Original reaction, substrates and products identifiers are retained, and omitted cofactors are disclosed.

The Online Template Generator was rebuilt to implement the updated extraction and encoding workflow. From a user-provided reaction (SMILES), the tool performs Transformer-based atom-atom mapping, mono-substrate decomposition, implicit-hydrogen encoding, and emits templates at radii 0–10, including cases derived from mass-imbalanced reactions when a coherent reaction centre exists. Generated templates appear in the same UI as the Explorer for immediate inspection, filtering, and export (TSV/CSV/JSON), ensuring that interactive exploration and on-the-fly generation share one consistent representation.
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Template Explorer preview. From top to bottom: the top Quick Search allows single filter search, examples enable user to easily test queries, while the Advanced Search panel gives access to all combination of search settings and sorting. Template records appear as cards with visual previews, summary chips (datasets/provenance, related reactions, ECs, sequence score, radius), inline SMARTS preview, one-click copy, and a link to the full detail view.</figcaption></figure>
## Programmatic access

The REST API has been redesigned to mirror the Template Explorer's multi-criteria search: filters for dataset, EC (exact or prefix such as "2.7.1"), radius (0–10), reaction IDs, and template IDs. Results return typed JSON, deduplicated by canonical SMARTS, with provenance counts and sequence-support scores when available. Documentation follows the OpenAPI specification and is published online with interactive forms, enabling direct, in-browser query testing. Paginatio and soft rate limits protect the service (default page size 200), while larger datasets are provided via the Downloads page. For high-throughput workflows, prebuilt per-dataset bundles mirror API fields to ensure bit-identical results with the online service.

## Interoperability and integrations

RetroRules provides reaction (MetaNetX and Rhea IDs), compound (MetaNetX and ChEBI), EC (BRENDA), and protein identifiers (UniProt) for external registries. Per-dataset provenance remains attached to templates.

Template identifiers adopt a deterministic, three-layer design: RR:AAAAAA-BBBBBB-CCCCC, where **A** hashes the substrate subgraph (heavy-atom connectivity), **B** hashes the product subgraph, and **C** hashes the canonical SMARTS of the template (including radius). The scheme, inspired by InChIKey layering, enables fast, MCS-free grouping and retrieval — for example, selecting all templates that share the same substrate motif via the **A** segment, or clustering alternative product contexts via **B** — while **C** identifies the complete mapped transformation. All template identifiers were verified to be unique across the entire release; no collisions were found.

Integration in workflows is provided via a RetroRules Galaxy node, which parameterizes queries (dataset, radius, EC filters, reaction or template IDs) and returns TSV/CSV/JSON suitable for batch pipelines. This node lowers the entry barrier for non-specialists while preserving full provenance in outputs, supporting routine pathway design, rule selection, and enables end-to-end, reproducible pipelines through Galaxy-base environments[34].

## Discussion

This update integrates consolidated biochemical resources (MetaNetX v4.4, Rhea release 139) with broader organic chemistry (USPTO-50k), expanding the template space by ~6.3x and EC coverage by ~1.7x. Extending encoding to radii 0 to 10 provides fine-grained control of specificity, with radius 0 capturing the reaction centre and higher radii enforcing context, which also supports modelling of enzymatic promiscuity. The shift to implicit-hydrogen encoding and minimal atomic primitives makes SMARTS more compact and faster to apply (~5–10x in practice), lowering computational costs for pathway search. Migration from an
MCS based approach to rxnmapper improves mapping accuracy, supporting more reliable decomposition and template generation. The website redesign improves the accessibility with faceted search by dataset, radius, EC numbers and reaction IDs, provenance-aware deduplication, sortable results, and per-template pages with exports and link-outs. The REST API mirrors these capabilities with typed filters and an OpenAPI guide that enables executable example queries.

Crucially for (bio-)retrosynthesis, multi radius rules enable control of the branching factor and route plausibility in backward search. Lower radii widen exploration and increase coverage of candidate disconnections, while higher radii constrain context to reduce false positives. Sequence evidence and EC constraints supply priors for ranking, and faster matching allows deeper or broader searches under fixed time constraints. In this context, a reaction rule database serves as an accessible entry point for scalable retrosynthesis and pathway design, offering consistent abstractions with controllable specificity, fast matching, and programmatic access for ranking, pruning, and integration into automated workflows.

Patent-driven chemistry and nature's metabolism occupy largely different neighbourhoods of reaction space. This is illustrated by the tiny overlap of UPSTO templates with the biochemical sets (1,510 shared templates which represents ~0.2% of USPTO templates), indicating complementarity rather than redundancy. This breadth helps connect biochemical reactivity to alternative contexts when needed, without being a prerequisite for the core biochemical use cases.

Two deliberate trade-offs deserve attention. First, templates from non-fully mass-balanced are included. Inclusion broadens biochemical coverage, as shown by the increased span across level-4 EC numbers and a better representation of enzyme classes. Second, stereochemistry is dropped relative to 2019. Stereochemistry in SMARTS is brittle across toolkits and sources, and consistent handling requires precise local context that is not uniformly annotated. Tools such as RDChiral[35] provide improved consistency on the template application side, yet heterogeneous encodings and incomplete stereochemical annotation in metabolic resources are uneven, limiting reliable propagation at scale.

Priorities for the next cycle include multi-criteria scoring (combining sequence support, structural context, and observation frequency), thermodynamic and stoichiometric quality checks to guide reaction selection and pathway ranking, and broader handling of cofactors and protonation states to improve comparability across datasets. These steps, coupled with stable identifiers, open formats, and programmatic access, aim to keep RetroRules a dependable substrate for pathway design, reaction prediction, and enzyme engineering at scale.
Conclusions

RetroRules 2026 delivers a unified, multi-dataset template resource that couples curated biochemistry (MetaNetX v4.4, Rhea rel. 139) with diverse organic reactivity (USPTO) under explicit provenance. Extended radii (0–10) provide tuneable specificity from centre-only to highly contextual patterns, supporting enzymatic promiscuity modelling, while implicit-hydrogen encoding and minimal atom primitives compress rules and accelerate application. Atom-atom mapping upgraded to rxnmapping improves accuracy, and sequence annotations are refreshed and normalized for ranking. Including reactions that are not fully mass-balanced expands biochemical coverage without constraining stoichiometry-sensitive analyses. A redesigned website, an overhauled Online Template Generator, and an OpenAPI-typed REST API expose faceted search, deduplicated provenance-aware views, stable identifiers, and convenient exports suited to automated pipelines. Versioned, DOI-backed bundles and accessible archives preserve reproducibility and long-term interoperability. Together, these advances provide a practical substrate for retrosynthesis, retrobiosynthesis, pathway discovery, and reaction prediction at scale, with next steps aimed at multi-criteria scoring, thermodynamic and stoichiometric quality signals, and generalized cofactor handling.

Data availability

RetroRules is available at https://retrorules.org with the Template Explorer, Online
Template Generator, and a documented REST API (/apidocs, OpenAPI). Downloads
provide dataset-specific bundles (MetaNetX, Rhea, USPTO) in TSV/CSV/JSON. Current
release: rr3.0.0; archives rr01-rr02 remain available. Data: CC BY 4.0; upstream
licenses apply. Releases have persistent DOIs (rr3.0.0 provided upon publication).

Supplementary Data

Curated biochemical cofactor list (originally from Duigou et al. [5]) and USPTO-50k
cofactor list (taken from Schneider et. al.[29]).

Acknowledgments

CRediT author statement:

Conceptualization: TD; Data curation: TD; Investigation: TD; Methodology: TD; Software: TD, PM; Supervision: TD; Validation: TD; Visualization: TD; Funding: JLF; Writing - original draft: TD; Writing - review & editing: all authors. All authors approved the final manuscript.
The authors thank Guillaume Gricourt for assistance in developing the Galaxy wrapper for querying the RetroRules API. The authors thank the curators of MetaNetX, Rhea, UniProt, ChEBI, BRENDA, and the USPTO-50k compilers. Appreciation to maintainers of RDKit, rxnmapper, and related open-source tools.

## Funding

This work was supported by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program, reference ANR-22-PEBB-0008.

## Competing interest

The authors declare no competing interests.

## References

1. Daylight Chemical Information Systems, Inc. Daylight Theory Manual. 2011. Report No.: v4.9. Available: https://www.daylight.com/dayhtml/doc/theory/

2. Gricourt G, Meyer P, Duigou T, Faulon J-L. Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review. ACS Synth Biol. 2024; acssynbio.4c00091. doi:10.1021/acssynbio.4c00091

3. Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metabolic Engineering. 2023;80: 130-141. doi:10.1016/j.ymben.2023.09.012

4. Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal. 2023;6: 137-151. doi:10.1038/s41929-022-00909-w

5. Duigou T, du Lac M, Carbonell P, Faulon J-L. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Res. 2019;47: D1229-D1235. doi:10.1093/nar/gky940

6. Hérisson J, Duigou T, Du Lac M, Bazi-Kabbaj K, Sabeti Azad M, Buldum G, et al. The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering. Nat Commun. 2022;13: 5082. doi:10.1038/s41467-022-32661-x

7. Balzerani F, Hinojosa-Nogueira D, Cendoya X, Blasco T, Pérez-Burillo S, Apaolaza I, et al. Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods. npj Syst Biol Appl. 2022;8: 24. doi:10.1038/s41540-022-00234-9
8. Martín Lázaro H, Marín Bautista R, Carbonell P. DetSpace: a web server for engineering detectable pathways for bio-based chemical production. Nucleic Acids Res. 2024;52: W476-W480. doi:10.1093/nar/gkae287

9. Singh KS, Duran HS, Pup ED, Delgado OZ, Wees SCM van, Hooft JJJ van der, et al. MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways. PLOS Biology. 2025;23: e3003307. doi:10.1371/journal.pbio.3003307

10. Blasco T, Pérez-Burillo S, Balzerani F, Hinojosa-Nogueira D, Lerma-Aguilera A, Pastoriza S, et al. An extended reconstruction of human gut microbiota metabolism of dietary compounds. Nat Commun. 2021;12: 4728. doi:10.1038/s41467-021-25056-x

11. Heid E, Goldman S, Sankaranarayanan K, Coley CW, Flamm C, Green WH. EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. J Chem Inf Model. 2021;61: 4949-4961. doi:10.1021/acs.jcim.1c00921

12. Balzerani F, Blasco T, Pérez-Burillo S, Valcarcel LV, Hassoun S, Planes FJ. Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota. npj Syst Biol Appl. 2024;10: 56. doi:10.1038/s41540-024-00381-1

13. Martin MR, Bittremieux W, Hassoun S. Molecular Structure Discovery for Untargeted Metabolomics Using Biotransformation Rules and Global Molecular Networking. Anal Chem. 2025;97: 3213-3219. doi:10.1021/acs.analchem.4c01565

14. Chum S, Naveira Montalvo A, Hassoun S. Computational analysis of the gut microbiota-mediated drug metabolism. Comput Struct Biotechnol J. 2025;27: 1472-1481. doi:10.1016/j.csbj.2025.03.016

15. Pandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, et al. Metabolic perceptrons for neural computing in biological systems. Nat Commun. 2019;10: 3880. doi:10.1038/s41467-019-11889-0

16. Brito LF, Irla M, Nærdal I, Le SB, Delépine B, Heux S, et al. Evaluation of Heterologous Biosynthetic Pathways for Methanol-Based 5-Aminovalerate Production by Thermophilic Bacillus methanolicus. Front Bioeng Biotechnol. 2021;9. doi:10.3389/fbioe.2021.686319

17. Liu Z, Zhang X, Lei D, Qiao B, Zhao G-R. Metabolic engineering of Escherichia coli for de novo production of 3-phenylpropanol via retrobiosynthesis approach. Microbial Cell Factories. 2021;20: 121. doi:10.1186/s12934-021-01615-1

18. Yiakoumetti A, Hanko EKR, Zou Y, Chua J, Chromy J, Stoney RA, et al. Expanding flavone and flavonol production capabilities in Escherichia coli. Front Bioeng Biotechnol. 2023;11. doi:10.3389/fbioe.2023.1275651
19. Moretti S, Tran VDT, Mehl F, Ibberson M, Pagni M. MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models. Nucleic Acids Res. 2021;49: D570–D574. doi:10.1093/nar/gkaa992

20. Bansal P, Morgat A, Axelsen KB, Muthukrishnan V, Coudert E, Aimo L, et al. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res. 2022;50: D693–D700. doi:10.1093/nar/gkab1016

21. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res. 2025;53: D609–D617. doi:10.1093/nar/gkae1010

22. Schwaller P, Hoover B, Reymond J-L, Strobelt H, Laino T. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Science Advances. 2021;7: eabe4166. doi:10.1126/sciadv.abe4166

23. Lin A, Dyubankova N, Madzhidov TI, Nugmanov RI, Verhoeven J, Gimadiev TR, et al. Atom-to-atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies. Molecular Informatics. 2022;41: 2100138. doi:10.1002/minf.202100138

24. Kim T, Lee S, Kwak Y, Choi M-S, Park J, Hwang SJ, et al. READRetro: natural product biosynthesis predicting with retrieval-augmented dual-view retrosynthesis. New Phytologist. 2024;243: 2512–2527. doi:10.1111/nph.20012

25. Tu Z, Choure SJ, Fong MH, Roh J, Levin I, Yu K, et al. ASKCOS: Open-Source, Data-Driven Synthesis Planning. Acc Chem Res. 2025;58: 1764–1775. doi:10.1021/acs.accounts.5c00155

26. Levin I, Liu M, Voigt CA, Coley CW. Merging enzymatic and synthetic chemistry with computational synthesis planning. Nat Commun. 2022;13: 7747. doi:10.1038/s41467-022-35422-y

27. Zheng S, Zeng T, Li C, Chen B, Coley CW, Yang Y, et al. Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP. Nat Commun. 2022;13: 3342. doi:10.1038/s41467-022-30970-9

28. Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res. 2016;44: D1214–D1219. doi:10.1093/nar/gkv1031

29. Schneider N, Stiefl N, Landrum GA. What's What: The (Nearly) Definitive Guide to Reaction Role Assignment. J Chem Inf Model. 2016;56: 2336–2346. doi:10.1021/acs.jcim.6b00564
30. Lowe DM. Extraction of chemical structures and reactions from the literature.
PhD thesis, University of Cambridge (Department of Chemistry). 2012. Available:
https://www/repository.cam.ac.uk/handle/1810/244727

31. Heid E, Liu J, Aude A, Green WH. Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications. J Chem Inf Model. 2022;62: 16-26. doi:10.1021/acs.jcim.1c01192

32. Rahman SA, Torrance G, Baldacci L, Martínez Cuesta S, Fenninger F, Gopal N, et al. Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics. 2016;32: 2065-2066. doi:10.1093/bioinformatics/btw096

33. Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 2021;49: D498-D508. doi:10.1093/nar/gkaa1025

34. The Galaxy Community. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res. 2024;52: W83-W94. doi:10.1093/nar/gkae410

35. Coley CW, Green WH, Jensen KF. RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application. J Chem Inf Model. 2019;59: 2529-2537. doi:10.1021/acs.jcim.9b00286