Evidence map›Paper›PMID 34750989›Full record

ArticleMolecular informatics2022

RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design.

Gian Marco Ghiandoni, Michael J Bodkin, Beining Chen, Dimitar Hristozov, James E A Wallace, James Webster, Valerie J Gillet

Abstract read
In one paragraph

Article in Molecular informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025
    Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Gian Marco GhiandoniInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.
Michael J BodkinEvotec (U.K.) Ltd, 114 Innovation Drive Milton Park, Abingdon, OX14 4RZ, UK.
Beining ChenChemistry Department, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK.
Dimitar HristozovEvotec (U.K.) Ltd, 114 Innovation Drive Milton Park, Abingdon, OX14 4RZ, UK.
James E A WallaceEvotec (U.K.) Ltd, 114 Innovation Drive Milton Park, Abingdon, OX14 4RZ, UK.ORCID 0000-0001-7681-2791
James WebsterInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.
Valerie J GilletInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/R505821/1
6 · The paper itself

Abstract

Reaction-based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction-based de novo design tool, which is based on a pseudo-retrosynthetic fragmentation of a reference ligand and an inside-out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool.

Indexed as

AlgorithmsLigandsRetrospective StudiesLigandsde novo drug designpatentspharmaceuticalsreaction informatics

Identifiers

PMID34750989
PMCPMC9285524

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.