Evidence map›Paper›PMID 38258328›Full record

ArticleMolecular informatics2024

Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.

Gian Marco Ghiandoni, Stuart R Flanagan, Michael J Bodkin, Maria Giulia Nizi, Albert Galera-Prat, Annalaura Brai, Beining Chen, James E A Wallace, Dimitar Hristozov, James Webster and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 14% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025
    Review
  2. 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

14 authors at 5 institutions in 3 countries.

Gian Marco GhiandoniInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.ORCID https://orcid.org/0000-0002-2592-2939
Stuart R FlanaganEvotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK.
Michael J BodkinEvotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK.
Maria Giulia NiziDepartment of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy.
Albert Galera-PratFaculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland.
Annalaura BraiDepartment of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100, Siena, Italy.
Beining ChenDepartment of Chemistry, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK.
James E A WallaceEvotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK.ORCID https://orcid.org/0000-0001-7681-2791
Dimitar HristozovEvotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK.
James WebsterInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.
Giuseppe ManfroniDepartment of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy.
Lari LehtiöFaculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland.
Oriana TabarriniDepartment of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy.
Valerie J GilletInformation School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.ORCID https://orcid.org/0000-0002-8403-3111
Evotec (United Kingdom) · GBUniversity of Sheffield · GBUniversity of Perugia · ITUniversity of Oulu · FIUniversity of Siena · IT

Funding

Biotechnology and Biological Sciences Research Council (BBSRC)Evotec BB/R505821/1Italian Association for Cancer Research (AIRC)
6 · The paper itself

Abstract

De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.

Indexed as

Drug DesignPoly(ADP-ribose) Polymerase InhibitorsHumansPoly (ADP-Ribose) Polymerase-1SoftwarePARP1 protein, humanPoly (ADP-Ribose) Polymerase-1Poly(ADP-ribose) Polymerase Inhibitorsde novo drug designPARP1 inhibitorspharmaceuticalsreaction informaticssynthetic accessibility

Identifiers

PMID38258328
PMCPMC11475289
OpenAlexW4391138303

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.