Combining MVD and Ridge Method to Predict CDK2 Inhibition.

Sema Nur Pehlivan, Amauri Duarte da Silva, Walter Filgueira de Azevedo

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. 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.

Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026
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.

Sema Nur PehlivanDepartment of Bioengineering, Institute of Science and Technology, Marmara University, Kadıköy, Istanbul, Turkey.

Amauri Duarte da SilvaGraduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, Brazil.

Walter Filgueira de AzevedoDepartment of Physics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, MG, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molegro Virtual Docker (MVD) is one of the most used docking programs to address protein-ligand interactions. It integrates different search algorithms and scoring functions, creating 16 combinations of these approaches. This flexibility allowed its application to a wide range of protein targets with successful prediction of inhibition based on docking results. MVD calculates binding affinity using MolDock and Plants scores, with outputs showing the energy terms employed in these scoring functions. We may use these docking results to build machine learning models to predict the inhibition of specific protein targets. In this chapter, we describe the integration of MVD and Scikit-Learn (Ridge regression method) and its application to predict the inhibition of cyclin-dependent kinase 2 (CDK2). This combination of techniques makes it possible to explore the concept of scoring function space and build an adequate computational model to predict binding affinity with superior predictive performance compared with a classical scoring function.

Indexed as

Computational BiologyCyclin-Dependent Kinase 2Molecular Docking SimulationProtein Kinase InhibitorsSoftwareAlgorithmsBinding SitesHumansLigandsMachine LearningProtein BindingCDK2 protein, humanCyclin-Dependent Kinase 2LigandsProtein Kinase InhibitorsArtificial intelligenceCDK2Machine learningMolegro Virtual DockerSAnDReS 2.0Scoring function space

Identifiers

PMID41075083

DOI10.1007/978-1-0716-4949-7_3

What OpenQuestion holds

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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.