{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:50:50Z","timestamp":1790139050444,"version":"4.0.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"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":["Mol Divers"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Fungal infections are an increasing global health issue. Despite available treatments, fungal resistance reduces medicine effectiveness. This research conducted QSAR analysis on fifty-one 4-aryl-2-hydrazinothiazole derivatives previously evaluated for antifungal activity. The QSAR model was derived from a hybrid method combining genetic algorithms (GA) and multiple linear regression (MLR). The analysis showed a negative correlation between pMIC and RDF100e, ITH, R4m+, RDF120s, and GATS8e. The model was validated using an external test set by the leave-one-out cross-validation method. Additionally, Y-randomization, MAE, and Golbraikh-Tropsha metrics assessed the model\u2019s applicability domain. The study offers an in-depth molecular descriptor interpretation through three methods: atomic pair distribution, substructure-based analysis, and molecular surface mapping with cumulative atomic contributions. These methods help identify favorable and unfavorable structural groupings. Key molecular features influencing antifungal activity were identified, particularly the spatial arrangement of N1-hydrazine and C4 fragments in the thiazole nucleus. The research highlights Van der Waals interactions, electronegative atoms in substituents, and electron-donating groups. To address the limitations of modeling a small dataset, we applied the novel ARKA approach\u2014based on Arithmetic Residuals in K-groups Analysis\u2014to reduce descriptor dimensionality while preserving chemical relevance and improving interpretability.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical abstract<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>\n                    In the era of artificial intelligence (AI), molecular descriptor interpretation remains crucial in QSAR studies, especially for small datasets, where AI methods are less effective. This study identifies key molecular descriptors governing the antifungal activity of thiazole derivatives against\n                    <jats:italic>Candida albicans<\/jats:italic>\n                    through QSAR modeling. The findings clarify molecular influences on bioactivity, providing a valuable alternative to AI for predicting compound efficacy.\n                  <\/jats:p>","DOI":"10.1007\/s11030-025-11404-2","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T13:44:36Z","timestamp":1764337476000},"page":"4635-4659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Interpretable Quantitative Structure\u2013Activity Relationship (QSAR) for identification of potent antifungal activity agents towards Candida albicans ATCC 2091"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7968-4665","authenticated-orcid":false,"given":"Mariusz","family":"Zapadka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2107-2719","authenticated-orcid":false,"given":"Krzysztof Zbigniew","family":"\u0141\u0105czkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8545-177X","authenticated-orcid":false,"given":"Anna","family":"Budzy\u0144ska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7294-9448","authenticated-orcid":false,"given":"Mateusz","family":"Maciejewski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Przemys\u0142aw","family":"Dekowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4480-7338","authenticated-orcid":false,"given":"Bogumi\u0142a","family":"Kupcewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"11404_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s12866-022-02460-x","volume":"22","author":"J Gil","year":"2022","unstructured":"Gil J, Solis M, Higa A, Davis SC (2022) Candida albicans infections: a novel porcine wound model to evaluate treatment efficacy. BMC Microbiol 22:45. https:\/\/doi.org\/10.1186\/s12866-022-02460-x","journal-title":"BMC Microbiol"},{"issue":"1","key":"11404_CR2","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/jof4010022","volume":"4","author":"J Richardson","year":"2018","unstructured":"Richardson J, Ho J, Naglik J (2018) Candida\u2013epithelial interactions. J Fungi 4(1):22. https:\/\/doi.org\/10.3390\/jof4010022","journal-title":"J Fungi"},{"key":"11404_CR3","unstructured":"World Health Organization (2022) WHO fungal priority pathogens list to guide research, development and public health action. WHO. https:\/\/www.who.int\/publications\/i\/item\/9789240060241"},{"key":"11404_CR4","doi-asserted-by":"publisher","first-page":"1061150","DOI":"10.3389\/ffunb.2023.1061150","volume":"4","author":"DR Giacobbe","year":"2023","unstructured":"Giacobbe DR, Mikulska M, Vena A, Di Pilato V, Magnasco L, Marchese A, Bassetti M (2023) Challenges in the diagnosis and treatment of candidemia due to multidrug-resistant Candida auris. Front Fungal Biol 4:1061150. https:\/\/doi.org\/10.3389\/ffunb.2023.1061150","journal-title":"Front Fungal Biol"},{"issue":"7","key":"11404_CR5","doi-asserted-by":"publisher","first-page":"e00053-22","DOI":"10.1128\/aac.00053-22","volume":"66","author":"SE Jacobs","year":"2022","unstructured":"Jacobs SE, Jacobs JL, Dennis EK, Taimur S, Rana M, Patel D, Gitman M, Patel G, Schaefer S, Iyer K, Moon J, Adams V, Lerner P, Walsh TJ, Zhu Y, Anower MR, Vaidya MM, Chaturvedi S, Chaturvedi V (2022) Candida auris pan-drug-resistant to four classes of antifungal agents. Antimicrob Agents Chemother 66(7):e00053-22. https:\/\/doi.org\/10.1128\/aac.00053-22","journal-title":"Antimicrob Agents Chemother"},{"key":"11404_CR6","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1080\/14756360701709094","volume":"23","author":"A \u00d6zdemir","year":"2008","unstructured":"\u00d6zdemir A, Turan-Zitouni G, Kaplancikli ZA, Demirci F, Iscan G (2008) Studies on hydrazone derivatives as antifungal agents. J Enzyme Inhib Med Chem 23:470\u2013475. https:\/\/doi.org\/10.1080\/14756360701709094","journal-title":"J Enzyme Inhib Med Chem"},{"key":"11404_CR7","doi-asserted-by":"publisher","first-page":"230490","DOI":"10.3389\/fmicb.2016.01995","volume":"7","author":"KR Healey","year":"2016","unstructured":"Healey KR, Ortigosa CJ, Shor E, Perlin DS (2016) Genetic drivers of multidrug resistance in Candida glabrata. Front Microbiol 7:230490. https:\/\/doi.org\/10.3389\/fmicb.2016.01995","journal-title":"Front Microbiol"},{"key":"11404_CR8","doi-asserted-by":"publisher","first-page":"e23738","DOI":"10.1002\/jcla.23738","volume":"35","author":"X Zuo","year":"2021","unstructured":"Zuo X, Liu Y, Cai X, Zhan L, Hu K (2021) Association of different Candida species with catheter-related candidemia, and the potential antifungal treatments against their adhesion properties and biofilm-forming capabilities. J Clin Lab Anal 35:e23738. https:\/\/doi.org\/10.1002\/jcla.23738","journal-title":"J Clin Lab Anal"},{"key":"11404_CR9","doi-asserted-by":"publisher","first-page":"256","DOI":"10.5578\/mb.24248","volume":"50","author":"H Aslan","year":"2016","unstructured":"Aslan H, G\u00fclmez D (2016) Investigation of the correlation between biofilm forming ability of urinary Candida isolates with the use of urinary catheters and change of antifungal susceptibility in the presence of biofilm. Mikrobiyol Bul 50:256\u2013265. https:\/\/doi.org\/10.5578\/mb.24248","journal-title":"Mikrobiyol Bul"},{"key":"11404_CR10","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.jgar.2023.10.019","volume":"35","author":"Y Feng","year":"2023","unstructured":"Feng Y, Lu H, Whiteway M, Jiang Y (2023) Understanding fluconazole tolerance in Candida albicans: implications for effective treatment of candidiasis and combating invasive fungal infections. J Glob Antimicrob Resist 35:314\u2013321. https:\/\/doi.org\/10.1016\/j.jgar.2023.10.019","journal-title":"J Glob Antimicrob Resist"},{"key":"11404_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1111\/myc.13191","volume":"64","author":"T Levinson","year":"2020","unstructured":"Levinson T, Dahan A, Novikov A, Paran Y, Berman J, Ben-Ami R (2020) Impact of tolerance to fluconazole on treatment response in Candida albicans bloodstream infection. Mycoses 64:78\u201385. https:\/\/doi.org\/10.1111\/myc.13191","journal-title":"Mycoses"},{"key":"11404_CR12","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1111\/ctr.12834","volume":"30","author":"MT Levine","year":"2016","unstructured":"Levine MT, Chandrasekar PH (2016) Adverse effects of voriconazole: over a decade of use. Clin Transplant 30:1377\u20131386. https:\/\/doi.org\/10.1111\/ctr.12834","journal-title":"Clin Transplant"},{"key":"11404_CR13","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s12281-020-00379-2","volume":"14","author":"AR Novak","year":"2020","unstructured":"Novak AR, Bradley ME, Kiser TH, Mueller SW (2020) Azole-resistant Aspergillus and echinocandin-resistant Candida: what are the treatment options? Curr Fungal Infect Rep 14:141\u2013152. https:\/\/doi.org\/10.1007\/s12281-020-00379-2","journal-title":"Curr Fungal Infect Rep"},{"key":"11404_CR14","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1093\/cid\/civ933","volume":"62","author":"PG Pappas","year":"2016","unstructured":"Pappas PG, Kauffman CA, Andes DR, Clancy CJ, Marr KA, Ostrosky-Zeichner L, Reboli AC, Schuster MG, Vazquez JA, Walsh TJ, Zaoutis TE, Sobel JD (2016) Clinical practice guideline for the management of candidiasis: 2016 update by the Infectious Diseases Society of America. Clin Infect Dis 62:e1\u2013e50. https:\/\/doi.org\/10.1093\/cid\/civ933","journal-title":"Clin Infect Dis"},{"key":"11404_CR15","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cmi.2019.03.028","volume":"25","author":"KE Pristov","year":"2019","unstructured":"Pristov KE, Ghannoum MA (2019) Resistance of Candida to azoles and echinocandins worldwide. Clin Microbiol Infect 25:792\u2013798. https:\/\/doi.org\/10.1016\/j.cmi.2019.03.028","journal-title":"Clin Microbiol Infect"},{"key":"11404_CR16","doi-asserted-by":"publisher","first-page":"e146","DOI":"10.1017\/S0950268823001346","volume":"151","author":"J Szekely","year":"2023","unstructured":"Szekely J, Rakchang W, Rattanaphan P, Kositpantawong N (2023) Fluconazole and echinocandin resistance of Candida species in invasive candidiasis at a university hospital during pre-COVID-19 and the COVID-19 outbreak. Epidemiol Infect 151:e146. https:\/\/doi.org\/10.1017\/S0950268823001346","journal-title":"Epidemiol Infect"},{"key":"11404_CR17","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1080\/21505594.2022.2123325","volume":"13","author":"H Bilal","year":"2022","unstructured":"Bilal H, Shafiq M, Hou B, Islam R, Khan MN, Khan RU, Zeng Y (2022) Distribution and antifungal susceptibility pattern of Candida species from mainland China: a systematic analysis. Virulence 13:1573\u20131589. https:\/\/doi.org\/10.1080\/21505594.2022.2123325","journal-title":"Virulence"},{"key":"11404_CR18","doi-asserted-by":"publisher","first-page":"3994","DOI":"10.3390\/molecules27133994","volume":"27","author":"MF Arshad","year":"2022","unstructured":"Arshad MF, Alam A, Alshammari AA, Alhazza MB, Alzimam IM, Alam MA, Mustafa G, Ansari MS, Alotaibi AM, Alotaibi AA, Kumar S, Asdaq SMB, Imran M, Deb PK, Venugopala KN, Jomah S (2022) Thiazole: a versatile standalone moiety contributing to the development of various drugs and biologically active agents. Molecules 27:3994. https:\/\/doi.org\/10.3390\/molecules27133994","journal-title":"Molecules"},{"key":"11404_CR19","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.ejmech.2018.03.083","volume":"151","author":"CI Lino","year":"2018","unstructured":"Lino CI, De Souza IG, Borelli BM, Matos TTS, Teixeira INS, Ramos JP, De Souza Fagundes EM, De Oliveira Fernandes P, Maltarollo VG, Johann S, De Oliveira RB (2018) Synthesis, molecular modeling studies and evaluation of antifungal activity of a novel series of thiazole derivatives. Eur J Med Chem 151:248\u2013260. https:\/\/doi.org\/10.1016\/j.ejmech.2018.03.083","journal-title":"Eur J Med Chem"},{"key":"11404_CR20","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.ejmech.2013.04.042","volume":"65","author":"S Carradori","year":"2013","unstructured":"Carradori S, Secci D, Bolasco A, Rivanera D, Mari E, Zicari A, Lotti LV, Bizzarri B (2013) Synthesis and cytotoxicity of novel (thiazol-2-yl)hydrazine derivatives as promising anti-Candida agents. Eur J Med Chem 65:102\u2013111. https:\/\/doi.org\/10.1016\/j.ejmech.2013.04.042","journal-title":"Eur J Med Chem"},{"key":"11404_CR21","doi-asserted-by":"publisher","first-page":"3435","DOI":"10.3390\/molecules24193435","volume":"24","author":"A-I Pricopie","year":"2019","unstructured":"Pricopie A-I, Ionu\u021b I, Marc G, Arseniu A-M, Vlase L, Grozav A, G\u0103in\u0103 LI, Vodnar DC, P\u00eern\u0103u A, Tiperciuc B, Oniga O (2019) Design and synthesis of novel 1,3-thiazole and 2-hydrazinyl-1,3-thiazole derivatives as anti-Candida agents: in vitro antifungal screening, molecular docking study, and spectroscopic investigation of their binding interaction with bovine serum albumin. Molecules 24:3435. https:\/\/doi.org\/10.3390\/molecules24193435","journal-title":"Molecules"},{"key":"11404_CR22","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ejmech.2012.04.006","volume":"53","author":"D Secci","year":"2012","unstructured":"Secci D, Bizzarri B, Bolasco A, Carradori S, D\u2019Ascenzio M, Rivanera D, Mari E, Polletta L, Zicari A (2012) Synthesis, anti-Candida activity, and cytotoxicity of new (4-(4-iodophenyl)thiazol-2-yl)hydrazine derivatives. Eur J Med Chem 53:246\u2013253. https:\/\/doi.org\/10.1016\/j.ejmech.2012.04.006","journal-title":"Eur J Med Chem"},{"key":"11404_CR23","doi-asserted-by":"publisher","first-page":"136229","DOI":"10.1016\/j.molstruc.2023.136229","volume":"1293","author":"NV Sadgir","year":"2023","unstructured":"Sadgir NV, Adole VA, Dhonnar SL, Jagdale BS (2023) Synthesis and biological evaluation of coumarin appended thiazole hybrid heterocycles: antibacterial and antifungal study. J Mol Struct 1293:136229. https:\/\/doi.org\/10.1016\/j.molstruc.2023.136229","journal-title":"J Mol Struct"},{"key":"11404_CR24","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.ejmech.2010.10.027","volume":"46","author":"F Chimenti","year":"2010","unstructured":"Chimenti F, Bizzarri B, Bolasco A, Secci D, Chimenti P, Granese A, Carradori S, D\u2019Ascenzio M, Lilli D, Rivanera D (2010) Synthesis and biological evaluation of novel 2,4-disubstituted-1,3-thiazoles as anti-Candida spp. agents. Eur J Med Chem 46:378\u2013382. https:\/\/doi.org\/10.1016\/j.ejmech.2010.10.027","journal-title":"Eur J Med Chem"},{"issue":"1","key":"11404_CR25","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1186\/s13321-024-00933-x","volume":"16","author":"P-Y Cao","year":"2024","unstructured":"Cao P-Y, He Y, Cui M-Y, Zhang X-M, Zhang Q, Zhang H-Y (2024) Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability. J Cheminform 16(1):133. https:\/\/doi.org\/10.1186\/s13321-024-00933-x","journal-title":"J Cheminform"},{"key":"11404_CR26","doi-asserted-by":"publisher","first-page":"133355","DOI":"10.1016\/j.jhazmat.2023.133355","volume":"465","author":"Z Huang","year":"2023","unstructured":"Huang Z, Yu J, He W, Yu J, Deng S, Yang C, Zhu W, Shao X (2023) AI-enhanced chemical paradigm: from molecular graphs to accurate prediction and mechanism. J Hazard Mater 465:133355. https:\/\/doi.org\/10.1016\/j.jhazmat.2023.133355","journal-title":"J Hazard Mater"},{"key":"11404_CR27","doi-asserted-by":"publisher","first-page":"2135","DOI":"10.1007\/s11030-024-10809-9","volume":"28","author":"A Gomatam","year":"2024","unstructured":"Gomatam A, Hirlekar BU, Singh KD, Murty US, Dixit VA (2024) Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis. Mol Divers 28:2135\u20132152. https:\/\/doi.org\/10.1007\/s11030-024-10809-9","journal-title":"Mol Divers"},{"key":"11404_CR28","doi-asserted-by":"publisher","first-page":"349","DOI":"10.3390\/pharmaceutics16030349","volume":"16","author":"N \u0141api\u0144ska","year":"2024","unstructured":"\u0141api\u0144ska N, Pac\u0142awski A, Szl\u0119k J, Mendyk A (2024) Integrated QSAR models for prediction of serotonergic activity: machine learning unveiling activity and selectivity patterns of molecular descriptors. Pharmaceutics 16:349. https:\/\/doi.org\/10.3390\/pharmaceutics16030349","journal-title":"Pharmaceutics"},{"key":"11404_CR29","doi-asserted-by":"publisher","first-page":"108324","DOI":"10.1016\/j.compbiolchem.2024.108324","volume":"115","author":"VH Masand","year":"2024","unstructured":"Masand VH, Al-Hussain S, Masand GS, Samad A, Gawali R, Jadhav S, Zaki MEA (2024) E-QSAR (explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray. Comput Biol Chem 115:108324. https:\/\/doi.org\/10.1016\/j.compbiolchem.2024.108324","journal-title":"Comput Biol Chem"},{"key":"11404_CR30","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1039\/D4EM00173G","volume":"26","author":"A Banerjee","year":"2024","unstructured":"Banerjee A, Roy K (2024) ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data. Environ Sci Process Impacts 26:991\u20131007. https:\/\/doi.org\/10.1039\/D4EM00173G","journal-title":"Environ Sci Process Impacts"},{"key":"11404_CR31","doi-asserted-by":"publisher","first-page":"2618","DOI":"10.1021\/acs.jcim.7b00274","volume":"57","author":"P Polishchuk","year":"2017","unstructured":"Polishchuk P (2017) Interpretation of quantitative structure\u2013activity relationship models: past, present, and future. J Chem Inf Model 57:2618\u20132639. https:\/\/doi.org\/10.1021\/acs.jcim.7b00274","journal-title":"J Chem Inf Model"},{"key":"11404_CR32","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1023\/a:1015952613760","volume":"7","author":"LB Kier","year":"1990","unstructured":"Kier LB, Hall LH (1990) An electrotopological-state index for atoms in molecules. Pharm Res 7:801\u2013807. https:\/\/doi.org\/10.1023\/a:1015952613760","journal-title":"Pharm Res"},{"key":"11404_CR33","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.2147\/DDDT.S365909","volume":"16","author":"RM Kassab","year":"2022","unstructured":"Kassab RM, Al-Hussain SA, Elleboudy NS, Albohy A, Zaki ME, Abouzid KA, Muhammad ZA (2022) Tackling microbial resistance with isatin-decorated thiazole derivatives: design, synthesis, and in vitro evaluation of antimicrobial and antibiofilm activity. Drug Des Devel Ther 16:2817\u20132832. https:\/\/doi.org\/10.2147\/DDDT.S365909","journal-title":"Drug Des Devel Ther"},{"key":"11404_CR34","doi-asserted-by":"publisher","first-page":"136411","DOI":"10.1016\/j.molstruc.2023.136411","volume":"1294","author":"SL Dhonnar","year":"2023","unstructured":"Dhonnar SL, Adole VA, Patil RH, Khairnar BB, Pawar TB, Sadgir NV, Jagdale BS (2023) Synthesis, spectral, antifungal, in silico molecular docking, ADME and DFT studies of some 2-(2-hydrazineyl)thiazole derivatives: computational and experimental perspective. J Mol Struct 1294:136411. https:\/\/doi.org\/10.1016\/j.molstruc.2023.136411","journal-title":"J Mol Struct"},{"key":"11404_CR35","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.3390\/ma14133500","volume":"14","author":"A Biernasiuk","year":"2021","unstructured":"Biernasiuk A, Banasiewicz A, Mas\u0142yk M, Martyna A, Janeczko M, Baranowska-\u0141\u0105czkowska A, Malm A, \u0141\u0105czkowski KZ (2021) Synthesis and physicochemical characterization of novel dicyclopropyl-thiazole compounds as nontoxic and promising antifungals. Materials 14:3500. https:\/\/doi.org\/10.3390\/ma14133500","journal-title":"Materials"},{"key":"11404_CR36","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1007\/s00044-018-2221-x","volume":"27","author":"KZ \u0141\u0105czkowski","year":"2018","unstructured":"\u0141\u0105czkowski KZ, Konklewska N, Biernasiuk A, Malm A, Sa\u0142at K, Furga\u0142a A, Dzitko K, Bekier A, Baranowska-\u0141\u0105czkowska A, Paneth A (2018) Thiazoles with cyclopropyl fragment as antifungal, anticonvulsant, and anti-Toxoplasma gondii agents: synthesis, toxicity evaluation, and molecular docking study. Med Chem Res 27:2125\u20132140. https:\/\/doi.org\/10.1007\/s00044-018-2221-x","journal-title":"Med Chem Res"},{"key":"11404_CR37","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s00044-019-02433-2","volume":"28","author":"A Biernasiuk","year":"2019","unstructured":"Biernasiuk A, Kawczy\u0144ska M, Berecka-Rycerz A, Rosada B, Gumieniczek A, Malm A, Dzitko K, \u0141\u0105czkowski KZ (2019) Synthesis, antimicrobial activity, and determination of the lipophilicity of ((cyclohex-3-enylmethylene)hydrazinyl)thiazole derivatives. Med Chem Res 28:2023\u20132036. https:\/\/doi.org\/10.1007\/s00044-019-02433-2","journal-title":"Med Chem Res"},{"key":"11404_CR38","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.molstruc.2015.12.033","volume":"1108","author":"KZ \u0141\u0105czkowski","year":"2016","unstructured":"\u0141\u0105czkowski KZ, Motylewska K, Baranowska-\u0141\u0105czkowska A, Biernasiuk A, Misiura K, Malm A, Fern\u00e1ndez B (2016) Synthesis, antimicrobial evaluation and theoretical prediction of NMR chemical shifts of thiazole and selenazole derivatives with high antifungal activity against Candida spp. J Mol Struct 1108:427\u2013437. https:\/\/doi.org\/10.1016\/j.molstruc.2015.12.033","journal-title":"J Mol Struct"},{"key":"11404_CR39","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1007\/s00044-017-1904-z","volume":"26","author":"KZ \u0141\u0105czkowski","year":"2017","unstructured":"\u0141\u0105czkowski KZ, Landowska K, Biernasiuk A, Sa\u0142at K, Furga\u0142a A, Plech T, Malm A (2017) Synthesis, biological evaluation and molecular docking studies of novel quinuclidinone derivatives as potential antimicrobial and anticonvulsant agents. Med Chem Res 26:2088\u20132104. https:\/\/doi.org\/10.1007\/s00044-017-1904-z","journal-title":"Med Chem Res"},{"key":"11404_CR40","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s00214-018-2214-3","volume":"137","author":"A Baranowska-\u0141\u0105czkowska","year":"2018","unstructured":"Baranowska-\u0141\u0105czkowska A, Kozak M, \u0141\u0105czkowski KZ, Fern\u00e1ndez B (2018) Theoretical calculation of NMR shifts in newly developed antibacterial 4-formylbenzoic acid-based thiazoles. Theor Chem Acc 137:46. https:\/\/doi.org\/10.1007\/s00214-018-2214-3","journal-title":"Theor Chem Acc"},{"key":"11404_CR41","unstructured":"Frisch MJ et al (2016) Gaussian 09, Revision A.02. Gaussian Inc., Wallingford CT"},{"key":"11404_CR42","doi-asserted-by":"publisher","unstructured":"Kitowski J, Tura\u0142a M, Wiatr K, Dutka \u0141 (2012) PL-Grid: foundations and perspectives of national computing infrastructure. Lect Notes Comput Sci 1\u201314. https:\/\/doi.org\/10.1007\/978-3-642-28267-6_1","DOI":"10.1007\/978-3-642-28267-6_1"},{"key":"11404_CR43","doi-asserted-by":"publisher","unstructured":"Zapadka M, \u0141\u0105czkowski KZ, Budzy\u0144ska A, Maciejewski M, Dekowski P, Kupcewicz B (2025) Thiazole [dataset]. https:\/\/doi.org\/10.17172\/NOMAD\/2025.11.24-1","DOI":"10.17172\/NOMAD\/2025.11.24-1"},{"key":"11404_CR44","doi-asserted-by":"publisher","unstructured":"Mauri A (2020) alvaDesc: a tool to calculate and analyze molecular descriptors and fingerprints. Methods Pharmacol Toxicol 801\u2013820. https:\/\/doi.org\/10.1007\/978-1-0716-0150-1_32","DOI":"10.1007\/978-1-0716-0150-1_32"},{"key":"11404_CR45","doi-asserted-by":"publisher","DOI":"10.1002\/9783527613106","volume-title":"Handbook of molecular descriptors","author":"R Todeschini","year":"2000","unstructured":"Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim. https:\/\/doi.org\/10.1002\/9783527613106"},{"key":"11404_CR46","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1007\/s00044-018-2136-6","volume":"27","author":"KZ \u0141\u0105czkowski","year":"2018","unstructured":"\u0141\u0105czkowski KZ et al (2018) Synthesis, molecular docking, ctDNA interaction, DFT calculation and evaluation of antiproliferative and anti-Toxoplasma gondii activities of 2,4-diaminotriazine-thiazole derivatives. Med Chem Res 27:1131\u20131148. https:\/\/doi.org\/10.1007\/s00044-018-2136-6","journal-title":"Med Chem Res"},{"key":"11404_CR47","doi-asserted-by":"publisher","first-page":"123836","DOI":"10.1016\/j.fuel.2022.123836","volume":"321","author":"AE Comesana","year":"2022","unstructured":"Comesana AE, Huntington TT, Scown CD, Niemeyer KE, Rapp VH (2022) A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties. Fuel 321:123836. https:\/\/doi.org\/10.1016\/j.fuel.2022.123836","journal-title":"Fuel"},{"key":"11404_CR48","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.chemolab.2014.05.010","volume":"136","author":"D Ballabio","year":"2014","unstructured":"Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R (2014) A novel variable reduction method adapted from space-filling designs. Chemom Intell Lab Syst 136:147\u2013154. https:\/\/doi.org\/10.1016\/j.chemolab.2014.05.010","journal-title":"Chemom Intell Lab Syst"},{"key":"11404_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chemolab.2015.07.007","volume":"147","author":"P Ambure","year":"2015","unstructured":"Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K (2015) \u201cNanoBRIDGES\u201d software: open access tools to perform QSAR and nano-QSAR modeling. Chemom Intell Lab Syst 147:1\u201313. https:\/\/doi.org\/10.1016\/j.chemolab.2015.07.007","journal-title":"Chemom Intell Lab Syst"},{"key":"11404_CR50","doi-asserted-by":"publisher","first-page":"4070","DOI":"10.1021\/acs.jcim.9b00476","volume":"59","author":"P Ambure","year":"2019","unstructured":"Ambure P, Gajewicz-Skretna A, Cordeiro MNDS, Roy K (2019) New workflow for QSAR model development from small data sets: small dataset curator and small dataset modeler. J Chem Inf Model 59:4070\u20134076. https:\/\/doi.org\/10.1021\/acs.jcim.9b00476","journal-title":"J Chem Inf Model"},{"key":"11404_CR51","doi-asserted-by":"publisher","first-page":"1800154","DOI":"10.1002\/minf.201800154","volume":"38","author":"A R\u00e1cz","year":"2019","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2019) Intercorrelation limits in molecular descriptor preselection for QSAR\/QSPR. Mol Inform 38:1800154. https:\/\/doi.org\/10.1002\/minf.201800154","journal-title":"Mol Inform"},{"key":"11404_CR52","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.chemolab.2016.01.008","volume":"152","author":"K Roy","year":"2016","unstructured":"Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures: further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18\u201333. https:\/\/doi.org\/10.1016\/j.chemolab.2016.01.008","journal-title":"Chemom Intell Lab Syst"},{"key":"11404_CR53","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.chemolab.2016.10.009","volume":"159","author":"K Roy","year":"2016","unstructured":"Roy K, Ambure P (2016) The \u201cdouble cross-validation\u201d software tool for MLR QSAR model development. Chemom Intell Lab Syst 159:108\u2013126. https:\/\/doi.org\/10.1016\/j.chemolab.2016.10.009","journal-title":"Chemom Intell Lab Syst"},{"key":"11404_CR54","doi-asserted-by":"publisher","first-page":"11392","DOI":"10.1021\/acsomega.8b01647","volume":"3","author":"K Roy","year":"2018","unstructured":"Roy K, Ambure P, Kar S (2018) How precise are our quantitative structure\u2013activity relationship derived predictions for new query chemicals? ACS Omega 3:11392\u201311406. https:\/\/doi.org\/10.1021\/acsomega.8b01647","journal-title":"ACS Omega"},{"key":"11404_CR55","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1021\/acs.jcim.6b00088","volume":"56","author":"P Gramatica","year":"2016","unstructured":"Gramatica P, Sangion A (2016) A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model 56:1127\u20131131. https:\/\/doi.org\/10.1021\/acs.jcim.6b00088","journal-title":"J Chem Inf Model"},{"key":"11404_CR56","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","volume":"20","author":"A Golbraikh","year":"2002","unstructured":"Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269\u2013276. https:\/\/doi.org\/10.1016\/S1093-3263(01)00123-1","journal-title":"J Mol Graph Model"},{"key":"11404_CR57","doi-asserted-by":"publisher","DOI":"10.1515\/9783110682045","volume-title":"Computational chemistry: applications and new technologies","author":"P Ramasami","year":"2021","unstructured":"Ramasami P (2021) Computational chemistry: applications and new technologies. Walter De Gruyter GmbH & Co KG, Berlin"},{"key":"11404_CR58","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1021\/acs.jcim.5b00206","volume":"55","author":"DLJ Alexander","year":"2015","unstructured":"Alexander DLJ, Tropsha A, Winkler DA (2015) Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316\u20131322. https:\/\/doi.org\/10.1021\/acs.jcim.5b00206","journal-title":"J Chem Inf Model"},{"issue":"11","key":"11404_CR59","doi-asserted-by":"publisher","first-page":"2200072","DOI":"10.1002\/minf.202200072","volume":"41","author":"P Kir\u00e1ly","year":"2022","unstructured":"Kir\u00e1ly P, Kiss R, Kov\u00e1cs D, Ballaj A, T\u00f3th G (2022) The relevance of goodness-of-fit, robustness and prediction validation categories of OECD-QSAR principles with respect to sample size and model type. Mol Inform 41(11):2200072. https:\/\/doi.org\/10.1002\/minf.202200072","journal-title":"Mol Inform"},{"key":"11404_CR60","doi-asserted-by":"publisher","unstructured":"OECD (2014) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. OECD series on testing and assessment No. 69. OECD Publishing. https:\/\/doi.org\/10.1787\/9789264085442-en","DOI":"10.1787\/9789264085442-en"},{"key":"11404_CR61","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1289\/ehp.5758","volume":"111","author":"L Eriksson","year":"2003","unstructured":"Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361\u20131375. https:\/\/doi.org\/10.1289\/ehp.5758","journal-title":"Environ Health Perspect"},{"key":"11404_CR62","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1021\/ci300084j","volume":"52","author":"N Chirico","year":"2012","unstructured":"Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2: new intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52:2044\u20132058. https:\/\/doi.org\/10.1021\/ci300084j","journal-title":"J Chem Inf Model"},{"key":"11404_CR63","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.3390\/molecules14051660","volume":"14","author":"PP Roy","year":"2009","unstructured":"Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660\u20131701. https:\/\/doi.org\/10.3390\/molecules14051660","journal-title":"Molecules"},{"key":"11404_CR64","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.chemolab.2011.03.011","volume":"107","author":"PK Ojha","year":"2011","unstructured":"Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107:194\u2013205. https:\/\/doi.org\/10.1016\/j.chemolab.2011.03.011","journal-title":"Chemom Intell Lab Syst"},{"key":"11404_CR65","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s00204-022-03252-y","volume":"96","author":"P De","year":"2022","unstructured":"De P, Kar S, Ambure P, Roy K (2022) Prediction reliability of QSAR models: an overview of various validation tools. Arch Toxicol 96:1279\u20131295. https:\/\/doi.org\/10.1007\/s00204-022-03252-y","journal-title":"Arch Toxicol"},{"key":"11404_CR66","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1590\/S0103-50532009000400021","volume":"20","author":"R Kiralj","year":"2009","unstructured":"Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770\u2013787. https:\/\/doi.org\/10.1590\/S0103-50532009000400021","journal-title":"J Braz Chem Soc"},{"key":"11404_CR67","doi-asserted-by":"publisher","first-page":"6576","DOI":"10.3390\/ijms23126576","volume":"23","author":"M Zapadka","year":"2022","unstructured":"Zapadka M, Dekowski P, Kupcewicz B (2022) HATS5m as an example of GETAWAY molecular descriptor in assessing the similarity\/diversity of the structural features of 4-thiazolidinone. Int J Mol Sci 23:6576. https:\/\/doi.org\/10.3390\/ijms23126576","journal-title":"Int J Mol Sci"}],"container-title":["Molecular Diversity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11030-025-11404-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11030-025-11404-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11030-025-11404-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T11:33:30Z","timestamp":1779536010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11030-025-11404-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,28]]},"references-count":67,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["11404"],"URL":"https:\/\/doi.org\/10.1007\/s11030-025-11404-2","relation":{},"ISSN":["1573-501X"],"issn-type":[{"value":"1573-501X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,28]]},"assertion":[{"value":"28 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}