Computational Study of Enzyme Inhibition.

Francisco das Chagas Pereira de Andrade, Mateus Henrique de Almeida da Costa, Anderson Nogueira Mendes

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Francisco das Chagas Pereira de AndradeLaboratory of Innovation in Science and Technology-LACITEC, Department of Biophysics and Physiology, Federal University of Piauí, 64049-550, Teresina, Piauí, Brazil.ORCID http://orcid.org/0000-0003-0141-2341

Mateus Henrique de Almeida da CostaLaboratory of Innovation in Science and Technology-LACITEC, Department of Biophysics and Physiology, Federal University of Piauí, 64049-550, Teresina, Piauí, Brazil.ORCID http://orcid.org/0000-0002-9866-4547

Anderson Nogueira MendesLaboratory of Innovation in Science and Technology-LACITEC, Department of Biophysics and Physiology, Federal University of Piauí, 64049-550, Teresina, Piauí, Brazil. anderson.mendes@ufpi.edu.br.ORCID http://orcid.org/0000-0002-9778-3667

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational approaches have become essential in modern drug discovery, significantly reducing the time and cost associated with identifying and optimizing new enzyme inhibitors. This chapter explores key computational techniques, including molecular docking, molecular dynamics, and QSAR modeling, which enhance the accuracy and efficiency of drug design. Furthermore, advancements in artificial intelligence and machine learning are increasingly integrated into these methodologies, improving predictive modeling and target validation. Despite challenges, such as system complexity and algorithm limitations, computational methods continue to evolve, bridging the gap between theoretical predictions and experimental validation. This chapter discusses the latest trends, software tools, and case studies, emphasizing their role in accelerating drug development and improving therapeutic outcomes.

Indexed as

Computational BiologyDrug DiscoveryEnzyme InhibitorsAlgorithmsDrug DesignHumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationQuantitative Structure-Activity RelationshipSoftwareEnzyme InhibitorsComputational drug discoveryEnzyme inhibitionMolecular dockingMolecular dynamicsQSAR modeling

Identifiers

PMID42562987

DOI10.1038/s41598-024-75100-1

What OpenQuestion holds

Textmetadata

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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.