{"doi":"10.1007/978-1-0716-4949-7_7","title":"Calculating Enzyme Inhibition with Random Forests","abstract":null,"journal":"Methods in Molecular Biology","year":2026,"id":645792,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1681728,"name":"Walter Filgueira de Azevedo","orcid":null,"position":1,"is_corresponding":false},{"id":1681727,"name":"Amauri Duarte da Silva","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Calculating Enzyme Inhibition with Random Forests","abstract":"Random forest is an advanced supervised machine learning method to build models for complex systems. This technique belongs to the ensemble method class, one of the most high-powered approaches in artificial intelligence. The flexibility of this technique allows its application to classification and regression tasks. Here, we aim to build a regression model to calculate binding affinity based on docked structures determined with the program Molegro Virtual Docker. A Google Colab workflow permits us to employ the docking results to generate regression models. Our code relies on the Random Forest method available in the Scikit-Learn library. We built a Random Forest model to predict the inhibition of cyclin-dependent kinase 2. This enzyme participates in the control of cell cycle progression and is a target for anticancer drugs. All datasets and Jupyter Notebooks with MVD4ML and SKReg4Model discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme . We made the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres .","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41075087","pmcid":null,"openalex_id":"https://openalex.org/W4415060165","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.60660468,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":"https://www.springernature.com/gp/researchers/text-and-data-mining","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/978-1-0716-4949-7_7","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-1-0716-4949-7_7","host_type":"book series"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41075087","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","Protein Structure and Dynamics","Bioinformatics and Genomic Networks"],"mesh_terms":["Machine Learning","Random Forest","Algorithms","Enzyme Inhibitors","Humans","Protein Binding","Software","Computational Biology","Cyclin-Dependent Kinase 2","Molecular Docking Simulation"],"keywords":["Random forest","Workflow","Regression","Support vector machine","Ensemble learning","Multi-label classification","Flexibility (engineering)","Regression analysis","Artificial intelligence","Complex Systems","Machine Learning","Ensemble Methods","Molegro Virtual Docker","Sandres 2.0"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T10:09:44.394861Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}