{"doi":"10.1016/j.csbj.2019.08.008","title":"Current computational methods for predicting protein interactions of natural products","abstract":null,"journal":"Computational and Structural Biotechnology Journal","year":2019,"id":688090,"datarank":0.6064576901751826,"base_score":4.04305126783455,"endowment":4.04305126783455,"self_citation_contribution":0.6064576901751826,"citation_network_contribution":0.0,"self_endowment_contribution":0.6064576901751826,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":56,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":6,"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":769279,"name":"Jianyu Li","orcid":"0000-0003-2577-1672","position":1,"is_corresponding":false},{"id":1797578,"name":"Pankaj Mishra","orcid":null,"position":2,"is_corresponding":false},{"id":670804,"name":"Mingjie Gao","orcid":"0000-0002-9129-0060","position":3,"is_corresponding":false},{"id":3233,"name":"Stefan Günther","orcid":"0000-0002-5594-4549","position":4,"is_corresponding":false},{"id":1797577,"name":"Aurélien F.A. Moumbock","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Current computational methods for predicting protein interactions of natural products","abstract":"Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.","is_dataset_classified":null,"base_score":4.04305126783455,"endowment":4.04305126783455,"datacite_reuse_total":6,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31762960","pmcid":null,"openalex_id":"https://openalex.org/W2982499649","authors":[],"funders":[{"funder_name":"Baden-Württemberg Stiftung","grant_id":"BWST_WSF-043","title":null},{"funder_name":"Deutscher Akademischer Austauschdienst","grant_id":"91653768","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"unidentified","title":"unidentified"}],"total_grants":3,"fwci":6.7588,"citation_percentile":0.97335743,"influential_citations":0,"citation_trend":[{"year":2020,"count":7},{"year":2021,"count":13},{"year":2022,"count":12},{"year":2023,"count":5},{"year":2024,"count":7},{"year":2025,"count":9},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.1016/j.csbj.2019.08.008","host_type":"journal"},{"url":"https://doi.org/10.1016/j.csbj.2019.08.008","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S2001037019301059?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S2001037019301059?httpAccept=text/plain","host_type":"publisher"},{"url":"https://spj.science.org/doi/pdf/10.1016/j.csbj.2019.08.008","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31762960","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6861622","host_type":"repository"},{"url":"https://doaj.org/article/b23f27937ba44c78bf8608697818cc8c","host_type":"repository"},{"url":"http://dx.doi.org/10.1016/j.csbj.2019.08.008","host_type":""},{"url":"https://dx.doi.org/10.1016/j.csbj.2019.08.008","host_type":""},{"url":"https://doi.org/https://doi.org/10.1016/j.csbj.2019.08.008","host_type":""}],"fields_of_study":["Computational Drug Discovery Methods","Microbial Natural Products and Biosynthesis","Plant biochemistry and biosynthesis","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":[],"keywords":["Druggability","Drug discovery","Computer science","Computational model","Computational biology","Drug target","Profiling (computer programming)","Chemical space","Drug development","Biochemical engineering","Machine learning","Artificial intelligence","Bioinformatics","Drug","Chemistry","Biology","Engineering","Pharmacology","610","Review Article","TP248.13-248.65","Biotechnology","Drug-target interactions","Natural products","Pharmacological space","Target fishing","Virtual screening"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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