{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T05:02:55Z","timestamp":1726290175888},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"Abstract<\/jats:title>The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between \u201cdruggable\u201d and \u201cundruggable\u201d proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein\u2013protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.<\/jats:p>","DOI":"10.1186\/s13321-023-00735-7","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T11:02:52Z","timestamp":1689764572000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PINNED: identifying characteristics of druggable human proteins using an interpretable neural network"],"prefix":"10.1186","volume":"15","author":[{"given":"Michael","family":"Cunningham","sequence":"first","affiliation":[]},{"given":"Danielle","family":"Pins","sequence":"additional","affiliation":[]},{"given":"Zolt\u00e1n","family":"Dezs\u0151","sequence":"additional","affiliation":[]},{"given":"Maricel","family":"Torrent","sequence":"additional","affiliation":[]},{"given":"Aparna","family":"Vasanthakumar","sequence":"additional","affiliation":[]},{"given":"Abhishek","family":"Pandey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"issue":"1","key":"735_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene Ontology: tool for the unification of biology. Nat Genet 25(1):25\u201329. https:\/\/doi.org\/10.1038\/75556","journal-title":"Nat Genet"},{"issue":"suppl","key":"735_CR2","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1093\/nar\/19.suppl.2247","volume":"19","author":"A Bairoch","year":"1991","unstructured":"Bairoch A, Boeckmann B (1991) The SWISS-PROT protein sequence data bank. Nucleic Acids Res 19(suppl):2247\u20132249. https:\/\/doi.org\/10.1093\/nar\/19.suppl.2247","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"735_CR3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1093\/bioinformatics\/btp002","volume":"25","author":"TM Bakheet","year":"2009","unstructured":"Bakheet TM, Doig AJ (2009) Properties and identification of human protein drug targets. Bioinformatics 25(4):451\u2013457","journal-title":"Bioinformatics"},{"issue":"1","key":"735_CR4","doi-asserted-by":"publisher","first-page":"10787","DOI":"10.1038\/s41598-020-67846-1","volume":"10","author":"A Bazaga","year":"2020","unstructured":"Bazaga A, Leggate D, Weisser H (2020) Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology. Sci Rep 10(1):10787. https:\/\/doi.org\/10.1038\/s41598-020-67846-1","journal-title":"Sci Rep"},{"issue":"3","key":"735_CR5","doi-asserted-by":"publisher","first-page":"e0117955","DOI":"10.1371\/journal.pone.0117955","volume":"10","author":"SC Bull","year":"2015","unstructured":"Bull SC, Doig AJ (2015) Properties of protein drug target classes. PLoS ONE 10(3):e0117955. https:\/\/doi.org\/10.1371\/journal.pone.0117955","journal-title":"PLoS ONE"},{"issue":"9","key":"735_CR6","doi-asserted-by":"publisher","first-page":"104883","DOI":"10.1016\/j.isci.2022.104883","volume":"25","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Lio\u2019 P, Moni MA, Shoombuatong W, Manavalan B (2022) Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. IScience 25(9):104883. https:\/\/doi.org\/10.1016\/j.isci.2022.104883","journal-title":"IScience"},{"issue":"14","key":"735_CR7","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","volume":"34","author":"Z Chen","year":"2018","unstructured":"Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, Webb GI, Smith AI, Daly RJ, Chou K-C, Song J (2018) iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 34(14):2499\u20132502. https:\/\/doi.org\/10.1093\/bioinformatics\/bty140","journal-title":"Bioinformatics"},{"issue":"9","key":"735_CR8","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/nrd894","volume":"1","author":"P Ch\u00e8ne","year":"2002","unstructured":"Ch\u00e8ne P (2002) ATPases as drug targets: learning from their structure. Nat Rev Drug Discov 1(9):665\u2013673. https:\/\/doi.org\/10.1038\/nrd894","journal-title":"Nat Rev Drug Discov"},{"issue":"3","key":"735_CR9","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/prot.1035","volume":"43","author":"K-C Chou","year":"2001","unstructured":"Chou K-C (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Genet 43(3):246\u2013255. https:\/\/doi.org\/10.1002\/prot.1035","journal-title":"Proteins Struct Funct Genet"},{"key":"735_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-11-S5-S9","author":"PR Costa","year":"2010","unstructured":"Costa PR, Acencio ML, Lemke N (2010) A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data. BMC Genomics. https:\/\/doi.org\/10.1186\/1471-2164-11-S5-S9","journal-title":"BMC Genomics"},{"issue":"2","key":"735_CR11","doi-asserted-by":"publisher","first-page":"167336","DOI":"10.1016\/j.jmb.2021.167336","volume":"434","author":"A David","year":"2022","unstructured":"David A, Islam S, Tankhilevich E, Sternberg MJE (2022) The AlphaFold database of protein structures: a biologist\u2019s guide. J Mol Biol 434(2):167336. https:\/\/doi.org\/10.1016\/j.jmb.2021.167336","journal-title":"J Mol Biol"},{"issue":"10","key":"735_CR12","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1093\/bioinformatics\/btaa968","volume":"37","author":"A de Falco","year":"2021","unstructured":"de Falco A, Dezso Z, Ceccarelli F, Cerulo L, Ciaramella A, Ceccarelli M (2021) Adaptive one-class gaussian processes allow accurate prioritization of oncology drug targets. Bioinformatics 37(10):1420\u20131427. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa968","journal-title":"Bioinformatics"},{"issue":"1","key":"735_CR13","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s12859-020-3442-9","volume":"21","author":"Z Dezs\u0151","year":"2020","unstructured":"Dezs\u0151 Z, Ceccarelli M (2020) Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinformatics 21(1):104. https:\/\/doi.org\/10.1186\/s12859-020-3442-9","journal-title":"BMC Bioinformatics"},{"key":"735_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1289259","author":"Y Feng","year":"2017","unstructured":"Feng Y, Wang Q, Wang T (2017) Drug target protein-protein interaction networks: a systematic perspective. BioMed Research International. https:\/\/doi.org\/10.1155\/2017\/1289259","journal-title":"BioMed Research International"},{"key":"735_CR15","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-017-1285-6","author":"E Ferrero","year":"2017","unstructured":"Ferrero E, Dunham I, Sanseau P (2017) In silico prediction of novel therapeutic targets using gene\u2013disease association data. J Transl Med. https:\/\/doi.org\/10.1186\/s12967-017-1285-6","journal-title":"J Transl Med"},{"issue":"5","key":"735_CR16","doi-asserted-by":"publisher","first-page":"e1003484","DOI":"10.1371\/journal.pgen.1003484","volume":"9","author":"B Georgi","year":"2013","unstructured":"Georgi B, Voight BF, Bu\u0107an M (2013) From mouse to Human: Evolutionary Genomics analysis of human orthologs of essential genes. PLoS Genet 9(5):e1003484. https:\/\/doi.org\/10.1371\/journal.pgen.1003484","journal-title":"PLoS Genet"},{"key":"735_CR17","doi-asserted-by":"publisher","first-page":"771808","DOI":"10.3389\/fphar.2021.771808","volume":"12","author":"Y Gong","year":"2021","unstructured":"Gong Y, Liao B, Wang P, Zou Q (2021) DrugHybrid_BS: using hybrid feature combined with Bagging-SVM to Predict potentially druggable proteins. Front Pharmacol 12:771808. https:\/\/doi.org\/10.3389\/fphar.2021.771808","journal-title":"Front Pharmacol"},{"issue":"7675","key":"735_CR18","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1038\/nature24277","volume":"550","author":"GTEx Consortium","year":"2017","unstructured":"GTEx Consortium (2017) Genetic effects on gene expression across human tissues. Nature 550(7675):204\u2013213. https:\/\/doi.org\/10.1038\/nature24277","journal-title":"Nature"},{"issue":"12","key":"735_CR19","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1038\/nrd.2016.184","volume":"15","author":"RK Harrison","year":"2016","unstructured":"Harrison RK (2016) Phase II and phase III failures: 2013\u20132015. Nat Rev Drug Discov 15(12):817\u2013818. https:\/\/doi.org\/10.1038\/nrd.2016.184","journal-title":"Nat Rev Drug Discov"},{"issue":"4","key":"735_CR20","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.jtbi.2009.11.002","volume":"262","author":"C Huang","year":"2010","unstructured":"Huang C, Zhang R, Chen Z, Jiang Y, Shang Z, Sun P, Zhang X, Li X (2010) Predict potential drug targets from the ion channel proteins based on SVM. J Theor Biol 262(4):750\u2013756. https:\/\/doi.org\/10.1016\/j.jtbi.2009.11.002","journal-title":"J Theor Biol"},{"issue":"5","key":"735_CR21","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.drudis.2016.01.007","volume":"21","author":"AA Jamali","year":"2016","unstructured":"Jamali AA, Ferdousi R, Razzaghi S, Li J, Safdari R, Ebrahimie E (2016) DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov Today 21(5):718\u2013724. https:\/\/doi.org\/10.1016\/j.drudis.2016.01.007","journal-title":"Drug Discov Today"},{"issue":"7","key":"735_CR22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13073-014-0057-7","volume":"6","author":"J Jeon","year":"2014","unstructured":"Jeon J, Nim S, Teyra J, Datti A, Wrana JL, Sidhu SS, Moffat J, Kim PM (2014) A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med 6(7):57. https:\/\/doi.org\/10.1186\/s13073-014-0057-7","journal-title":"Genome Med"},{"issue":"7873","key":"735_CR23","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, \u017d\u00eddek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman R, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583\u2013589. https:\/\/doi.org\/10.1038\/s41586-021-03819-2","journal-title":"Nature"},{"key":"735_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1639-3","author":"B Kim","year":"2017","unstructured":"Kim B, Jo J, Han J, Park C, Lee H (2017) In silico re-identification of properties of drug target proteins. BMC Bioinform. https:\/\/doi.org\/10.1186\/s12859-017-1639-3","journal-title":"BMC Bioinform"},{"issue":"1","key":"735_CR25","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1186\/1471-2105-10-168","volume":"10","author":"V Le Guilloux","year":"2009","unstructured":"Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10(1):168. https:\/\/doi.org\/10.1186\/1471-2105-10-168","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"735_CR26","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1186\/1471-2105-8-353","volume":"8","author":"Q Li","year":"2007","unstructured":"Li Q, Lai L (2007) Prediction of potential drug targets based on simple sequence properties. BMC Bioinformatics 8(1):353. https:\/\/doi.org\/10.1186\/1471-2105-8-353","journal-title":"BMC Bioinformatics"},{"key":"735_CR27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.aca.2015.02.032","volume":"871","author":"Z-C Li","year":"2015","unstructured":"Li Z-C, Zhong W-Q, Liu Z-Q, Huang M-H, Xie Y, Dai Z, Zou X-Y (2015) Large-scale identification of potential drug targets based on the topological features of human protein\u2013protein interaction network. Anal Chim Acta 871:18\u201327. https:\/\/doi.org\/10.1016\/j.aca.2015.02.032","journal-title":"Anal Chim Acta"},{"key":"735_CR28","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.artmed.2019.07.005","volume":"98","author":"J Lin","year":"2019","unstructured":"Lin J, Chen H, Li S, Liu Y, Li X, Yu B (2019) Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier. Artif Intell Med 98:35\u201347. https:\/\/doi.org\/10.1016\/j.artmed.2019.07.005","journal-title":"Artif Intell Med"},{"issue":"12","key":"735_CR29","doi-asserted-by":"publisher","first-page":"e1004597","DOI":"10.1371\/journal.pcbi.1004597","volume":"11","author":"C Mitsopoulos","year":"2015","unstructured":"Mitsopoulos C, Schierz AC, Workman P, Al-Lazikani B (2015) Distinctive behaviors of Druggable Proteins in Cellular Networks. PLoS Comput Biol 11(12):e1004597. https:\/\/doi.org\/10.1371\/journal.pcbi.1004597","journal-title":"PLoS Comput Biol"},{"issue":"D1","key":"735_CR30","doi-asserted-by":"publisher","first-page":"D1353","DOI":"10.1093\/nar\/gkac1046","volume":"51","author":"D Ochoa","year":"2023","unstructured":"Ochoa D, Hercules A, Carmona M, Suveges D, Baker J, Malangone C, Lopez I, Miranda A, Cruz-Castillo C, Fumis L, Bernal-Llinares M, Tsukanov K, Cornu H, Tsirigos K, Razuvayevskaya O, Buniello A, Schwartzentruber J, Karim M, Ariano B, Osorio REM, Ferrer J, Ge X, Machlitt-Northen S, Gonzalez-Uriarte A, Saha S, Tirunagari S, Mehta C, Rold\u00e1n-Romero JM, Horswell S, Young S, Ghoussaini M, Hulcoop DG, Dunham I, McDonagh EM (2023) The next-generation open targets platform: reimagined, redesigned, rebuilt. Nucleic Acids Res 51(D1):D1353\u2013D1359. https:\/\/doi.org\/10.1093\/nar\/gkac1046","journal-title":"Nucleic Acids Res"},{"issue":"7\u20138","key":"735_CR31","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/s00335-019-09809-0","volume":"30","author":"TI Oprea","year":"2019","unstructured":"Oprea TI (2019) Exploring the dark genome: implications for precision medicine. Mamm Genome 30(7\u20138):192\u2013200. https:\/\/doi.org\/10.1007\/s00335-019-09809-0","journal-title":"Mamm Genome"},{"key":"735_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-022-04245-4","author":"A Raies","year":"2022","unstructured":"Raies A, Tulodziecka E, Stainer J, Middleton L, Dhindsa RS, Hill P, Engkvist O, Harper AR, Petrovski S, Vitsios D (2022) DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Commun Biol. https:\/\/doi.org\/10.1038\/s42003-022-04245-4","journal-title":"Commun Biol"},{"issue":"5","key":"735_CR33","doi-asserted-by":"publisher","first-page":"e1006142","DOI":"10.1371\/journal.pcbi.1006142","volume":"14","author":"AD Rouillard","year":"2018","unstructured":"Rouillard AD, Hurle MR, Agarwal P (2018) Systematic interrogation of diverse omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets. PLoS Comput Biol 14(5):e1006142. https:\/\/doi.org\/10.1371\/journal.pcbi.1006142","journal-title":"PLoS Comput Biol"},{"issue":"D1","key":"735_CR34","doi-asserted-by":"publisher","first-page":"D1334","DOI":"10.1093\/nar\/gkaa993","volume":"49","author":"TK Sheils","year":"2021","unstructured":"Sheils TK, Mathias SL, Kelleher KJ, Siramshetty VB, Nguyen D-T, Bologa CG, Jensen LJ, Vidovi\u0107 D, Koleti A, Sch\u00fcrer SC, Waller A, Yang JJ, Holmes J, Bocci G, Southall N, Dharkar P, Math\u00e9 E, Simeonov A, Oprea TI (2021) TCRD and Pharos 2021: mining the human proteome for disease biology. Nucleic Acids Res 49(D1):D1334\u2013D1346. https:\/\/doi.org\/10.1093\/nar\/gkaa993","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"735_CR35","doi-asserted-by":"publisher","first-page":"5505","DOI":"10.1038\/s41598-022-09484-3","volume":"12","author":"R Sikander","year":"2022","unstructured":"Sikander R, Ghulam A, Ali F (2022) XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set. Sci Rep 12(1):5505. https:\/\/doi.org\/10.1038\/s41598-022-09484-3","journal-title":"Sci Rep"},{"issue":"4","key":"735_CR36","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/s40484-018-0157-2","volume":"6","author":"T Sun","year":"2018","unstructured":"Sun T, Lai L, Pei J (2018) Analysis of protein features and machine learning algorithms for prediction of druggable proteins. Quant Biology 6(4):334\u2013343. https:\/\/doi.org\/10.1007\/s40484-018-0157-2","journal-title":"Quant Biology"},{"issue":"D1","key":"735_CR37","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/nar\/gky1131","volume":"47","author":"D Szklarczyk","year":"2019","unstructured":"Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, von Mering C (2019) STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607\u2013D613. https:\/\/doi.org\/10.1093\/nar\/gky1131","journal-title":"Nucleic Acids Res"},{"issue":"6220","key":"735_CR38","doi-asserted-by":"publisher","first-page":"1260419","DOI":"10.1126\/science.1260419","volume":"347","author":"M Uhl\u00e9n","year":"2015","unstructured":"Uhl\u00e9n M, Fagerberg L, Hallstr\u00f6m BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson \u00c3, Kampf C, Sj\u00f6stedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA-K, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist P-H, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pont\u00e9n F (2015) Tissue-based map of the human proteome. Science 347(6220):1260419. https:\/\/doi.org\/10.1126\/science.1260419","journal-title":"Science"},{"issue":"8","key":"735_CR39","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1016\/j.celrep.2018.10.100","volume":"25","author":"BL Updegraff","year":"2018","unstructured":"Updegraff BL, Zhou X, Guo Y, Padanad MS, Chen P-H, Yang C, Sudderth J, Rodriguez-Tirado C, Girard L, Minna JD, Mishra P, DeBerardinis RJ, O\u2019Donnell KA (2018) Transmembrane protease TMPRSS11B promotes lung cancer growth by enhancing lactate export and glycolytic metabolism. Cell Rep 25(8):2223-2233e6. https:\/\/doi.org\/10.1016\/j.celrep.2018.10.100","journal-title":"Cell Rep"},{"issue":"1","key":"735_CR40","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1186\/s12859-021-04342-x","volume":"22","author":"A Viacava Follis","year":"2021","unstructured":"Viacava Follis A (2021) Centrality of drug targets in protein networks. BMC Bioinform 22(1):527. https:\/\/doi.org\/10.1186\/s12859-021-04342-x","journal-title":"BMC Bioinform"},{"issue":"9","key":"735_CR41","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1001\/jama.2020.1166","volume":"323","author":"OJ Wouters","year":"2020","unstructured":"Wouters OJ, McKee M, Luyten J (2020) Estimated research and development investment needed to bring a new medicine to market, 2009\u20132018. JAMA 323(9):844. https:\/\/doi.org\/10.1001\/jama.2020.1166","journal-title":"JAMA"},{"issue":"12","key":"735_CR42","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1038\/nrd2983","volume":"8","author":"H Wulff","year":"2009","unstructured":"Wulff H, Castle NA, Pardo LA (2009) Voltage-gated potassium channels as therapeutic targets. Nat Rev Drug Discov 8(12):982\u20131001. https:\/\/doi.org\/10.1038\/nrd2983","journal-title":"Nat Rev Drug Discov"},{"issue":"2","key":"735_CR43","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1101\/gr.6888208","volume":"18","author":"L Yao","year":"2008","unstructured":"Yao L, Rzhetsky A (2008) Quantitative systems-level determinants of human genes targeted by successful drugs. Genome Res 18(2):206\u2013213. https:\/\/doi.org\/10.1101\/gr.6888208","journal-title":"Genome Res"},{"issue":"5","key":"735_CR44","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1110\/ps.03479604","volume":"13","author":"C-S Yu","year":"2004","unstructured":"Yu C-S, Lin C-J, Hwang J-K (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n -peptide compositions. Protein Sci 13(5):1402\u20131406. https:\/\/doi.org\/10.1110\/ps.03479604","journal-title":"Protein Sci"},{"issue":"D1","key":"735_CR45","doi-asserted-by":"publisher","first-page":"D1398","DOI":"10.1093\/nar\/gkab953","volume":"50","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y (2022) Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res 50(D1):D1398\u2013D1407. https:\/\/doi.org\/10.1093\/nar\/gkab953","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"735_CR46","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1080\/10611860903046610","volume":"17","author":"M Zhu","year":"2009","unstructured":"Zhu M, Gao L, Li X, Liu Z, Xu C, Yan Y, Walker E, Jiang W, Su B, Chen X, Lin H (2009) The analysis of the drug\u2013targets based on the topological properties in the human protein\u2013protein interaction network. J Drug Target 17(7):524\u2013532. https:\/\/doi.org\/10.1080\/10611860903046610","journal-title":"J Drug Target"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00735-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00735-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00735-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T06:40:55Z","timestamp":1700376055000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00735-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,19]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["735"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00735-7","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,19]]},"assertion":[{"value":"23 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"64"}}