Extremely Randomized Trees to Determine Binding Affinity.

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. 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

2 authors.

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

Merging artificial intelligence and computational systems biology has the potential to address complex systems with a holistic view. This integration allows us to build robust regression models to predict enzyme inhibition using data obtained from docking simulations. This work adopts a hands-on approach to generate regression models to predict the inhibition of cyclin-dependent kinase 2. This protein is an anticancer drug target, and we have data for crystallographic structures and inhibition of this enzyme. We employed the Extremely Randomized Trees method implemented in the program SAnDReS 2.0 to predict CDK2 inhibition. Our regression models employ docking results obtained using protein-ligand docking programs Molegro Virtual Docker and AutoDock Vina 1.2. Our Extra Trees regression models showed superior performance compared with other machine learning techniques. All CDK2 datasets and a Jupyter Notebook with SKReg4Model discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme . We made the source code of the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres .

Indexed as

Computational BiologyCyclin-Dependent Kinase 2Molecular Docking SimulationProtein Kinase InhibitorsBinding SitesHumansLigandsMachine LearningProtein BindingSoftwareCDK2 protein, humanCyclin-Dependent Kinase 2LigandsProtein Kinase InhibitorsArtificial intelligenceComplex systemsExtra TreesMachine learningSAnDReS 2.0Scoring function space

Identifiers

PMID41075088

DOI10.1007/978-1-0716-4949-7_8

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.