Evidence map›Paper›PMID 39249497›Full record

ArticleJournal of chemical information and modeling2024

DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.

Milo Roucairol, Alexios Georgiou, Tristan Cazenave, Filippo Prischi, Olivier E Pardo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Milo RoucairolLAMSADE, Université Paris-Dauphine, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France.
Alexios GeorgiouLAMSADE, Université Paris-Dauphine, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France.
Tristan CazenaveLAMSADE, Université Paris-Dauphine, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France.
Filippo PrischiRandall Centre for Cell and Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London SE1 1UL, United Kingdom.ORCID 0000-0003-2107-938X
Olivier E PardoDivision of Cancer, Department of Surgery and Cancer, Imperial College, Du Cane Road, London W12 0NN, United Kingdom.ORCID 0000-0003-2223-1435

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing number of deep learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these neural network approaches design molecules to specifically bind a target based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for

Indexed as

Monte Carlo MethodAlgorithmsDrug DesignPharmaceutical PreparationsSmall Molecule LibrariesPharmaceutical PreparationsSmall Molecule Libraries

Identifiers

PMID39249497
PMCPMC11423341

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.