Evidence map›Paper›PMID 39162960›Full record

ArticleMolecular diversity2024

Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.

Jitendra Kuldeep, Neeraj Chaturvedi, Dinesh Gupta

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular diversity, 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

3 authors.

Jitendra KuldeepTranslational Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), New Delhi, 110067, India.
Neeraj ChaturvediTranslational Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), New Delhi, 110067, India.
Dinesh GuptaTranslational Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), New Delhi, 110067, India. dinesh@icgeb.res.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative machine learning models offer a novel strategy for chemogenomics and de novo drug design, allowing researchers to streamline their exploration of the chemical space and concentrate on specific regions of interest. In cases with limited inhibitor data available for the target of interest, de novo drug design plays a crucial role. In this study, we utilized a package called 'mollib,' trained on ChEMBL data containing approximately 365,000 bioactive molecules. By leveraging transfer learning techniques with this package, we generated a series of compounds, starting from five initial compounds, which are potential Plasmodium falciparum (Pf) Lactate dehydrogenase inhibitors. The resulting compounds exhibit structural diversity and hold promise as potential novel Pf Lactate dehydrogenase inhibitors.

Indexed as

AntimalarialsDrug DesignEnzyme InhibitorsL-Lactate DehydrogenaseMachine LearningPlasmodium falciparumSmall Molecule LibrariesAntimalarialsEnzyme InhibitorsL-Lactate DehydrogenaseSmall Molecule LibrariesDeep learningGenerative modelMolecular dockingMolecular dynamics simulationTransfer learning

Identifiers

PMID39162960

What OpenQuestion holds

Textmetadata
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.