{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:54:45Z","timestamp":1784292885000,"version":"3.55.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central South University Innovation-Driven Research Program","award":["2023CXQD004"],"award-info":[{"award-number":["2023CXQD004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22003078"],"award-info":[{"award-number":["22003078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"publisher","award":["MYRG-CRG2022-00008-ICMS"],"award-info":[{"award-number":["MYRG-CRG2022-00008-ICMS"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Macau FDCT","award":["0108\/2021\/A"],"award-info":[{"award-number":["0108\/2021\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10\u00a0years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https:\/\/formulationai.computpharm.org\/.<\/jats:p>","DOI":"10.1093\/bib\/bbad419","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T17:27:21Z","timestamp":1700674041000},"source":"Crossref","is-referenced-by-count":68,"title":["FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence"],"prefix":"10.1093","volume":"25","author":[{"given":"Jie","family":"Dong","sequence":"first","affiliation":[{"name":"Central South University Xiangya School of Pharmaceutical Sciences, , Changsha, China"},{"name":"Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau , Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau , Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanle","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Macau Faculty of Science and Technology, , Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8052-4773","authenticated-orcid":false,"given":"Defang","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau , Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"key":"2024011119352495000_ref1","doi-asserted-by":"crossref","first-page":"120554","DOI":"10.1016\/j.ijpharm.2021.120554","article-title":"Industry 4.0 for pharmaceutical manufacturing: preparing for the smart factories of the future","volume":"602","author":"Arden","year":"2021","journal-title":"Int J Pharm"},{"key":"2024011119352495000_ref2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1186\/s12967-021-02910-6","article-title":"Translational precision medicine: an industry perspective","volume":"19","author":"Hartl","year":"2021","journal-title":"J Transl Med"},{"key":"2024011119352495000_ref3","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1016\/j.drudis.2020.07.024","article-title":"High-throughput screening: today\u2019s biochemical and cell-based approaches","volume":"25","author":"Blay","year":"2020","journal-title":"Drug Discov Today"},{"key":"2024011119352495000_ref4","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.3390\/ijms23031620","article-title":"Virtual combinatorial chemistry and pharmacological screening: a short guide to drug design","volume":"23","author":"Suay-Garc\u00eda","year":"2022","journal-title":"Int J Mol Sci"},{"key":"2024011119352495000_ref5","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1146\/annurev-pharmtox-010919-023324","article-title":"Big data and artificial intelligence modeling for drug discovery","volume":"60","author":"Zhu","year":"2020","journal-title":"Annu Rev Pharmacol Toxicol"},{"key":"2024011119352495000_ref6","doi-asserted-by":"crossref","first-page":"bbab430","DOI":"10.1093\/bib\/bbab430","article-title":"Artificial intelligence in drug discovery: applications and techniques","volume":"23","author":"Deng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024011119352495000_ref7","doi-asserted-by":"crossref","first-page":"7904","DOI":"10.1038\/s41598-019-44264-6","article-title":"Target identification, screening and in vivo evaluation of pyrrolone-fused benzosuberene compounds against human epilepsy using zebrafish model of pentylenetetrazol-induced seizures","volume":"9","author":"Tanwar","year":"2019","journal-title":"Sci Rep"},{"key":"2024011119352495000_ref8","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1002\/cbf.3709","article-title":"Computational targeting of allosteric site of MEK1 by quinoline-based molecules","volume":"40","author":"Singh","year":"2022","journal-title":"Cell Biochem Funct"},{"key":"2024011119352495000_ref9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jconrel.2021.08.030","article-title":"Computational pharmaceutics - a new paradigm of drug delivery","volume":"338","author":"Wang","year":"2021","journal-title":"J Control Release Off J Control Release Soc"},{"key":"2024011119352495000_ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2021.05.016","article-title":"Machine learning directed drug formulation development","volume":"175","author":"Bannigan","year":"2021","journal-title":"Adv Drug Deliv Rev"},{"key":"2024011119352495000_ref11","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmgm.2021.108051","article-title":"Formulation design and mechanism study of hydrogel based on computational pharmaceutics theories","volume":"110","author":"Dai","year":"2022","journal-title":"J Mol Graph Model"},{"key":"2024011119352495000_ref12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ejpb.2019.02.007","article-title":"Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning","volume":"137","author":"Hossain","year":"2019","journal-title":"Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV"},{"key":"2024011119352495000_ref13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/B978-0-12-818438-7.00004-6","article-title":"Chapter 4 - applications of artificial intelligence in drug delivery and pharmaceutical development","author":"Colombo","year":"2020","journal-title":"Artif Intell Healthc"},{"key":"2024011119352495000_ref14","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jconrel.2011.11.029","article-title":"Quantitative structure-property relationship modeling of remote liposome loading of drugs","volume":"160","author":"Cern","year":"2012","journal-title":"J Control Release Soc"},{"key":"2024011119352495000_ref15","doi-asserted-by":"crossref","first-page":"2800","DOI":"10.1021\/mp500740d","article-title":"Computer-assisted drug formulation design: novel approach in drug delivery","volume":"12","author":"Metwally","year":"2015","journal-title":"Mol Pharm"},{"key":"2024011119352495000_ref16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ejpb.2020.12.018","article-title":"Design of novel orotransmucosal vaccine-delivery platforms using artificial intelligence","volume":"159","author":"Garcia-Del Rio","year":"2021","journal-title":"Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV"},{"key":"2024011119352495000_ref17","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3390\/pharmaceutics2020182","article-title":"Optimization of salbutamol sulfate dissolution from sustained release matrix formulations using an artificial neural network","volume":"2","author":"Chaibva","year":"2010","journal-title":"Pharmaceutics"},{"key":"2024011119352495000_ref18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10462-018-09679-z","article-title":"Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey","volume":"52","author":"Nguyen","year":"2019","journal-title":"Artif Intell Rev"},{"key":"2024011119352495000_ref19","doi-asserted-by":"crossref","first-page":"15923","DOI":"10.1007\/s10489-022-04278-6","article-title":"Front-end deep learning web apps development and deployment: a review","volume":"53","author":"Goh","year":"2023","journal-title":"Appl Intell Dordr Neth"},{"key":"2024011119352495000_ref20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10664-020-09894-9","article-title":"Automated end-to-end management of the modeling lifecycle in deep learning","volume":"26","author":"Gharibi","year":"2021","journal-title":"Empir Softw Eng"},{"key":"2024011119352495000_ref21","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial intelligence in drug discovery and development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov Today"},{"key":"2024011119352495000_ref22","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.apsb.2019.04.004","article-title":"Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques","volume":"9","author":"Zhao","year":"2019","journal-title":"Acta Pharm Sin B"},{"key":"2024011119352495000_ref23","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1007\/s11095-012-0717-5","article-title":"A practical method to predict physical stability of amorphous solid dispersions","volume":"29","author":"Greco","year":"2012","journal-title":"Pharm Res"},{"key":"2024011119352495000_ref24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jconrel.2019.08.030","article-title":"Predicting physical stability of solid dispersions by machine learning techniques","volume":"311-312","author":"Han","year":"2019","journal-title":"J Control Release Off J Control Release Soc"},{"key":"2024011119352495000_ref25","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ajps.2014.09.004","article-title":"A review on phospholipids and their main applications in drug delivery systems","volume":"10","author":"Li","year":"2015","journal-title":"Asian J Pharm Sci"},{"key":"2024011119352495000_ref26","doi-asserted-by":"crossref","DOI":"10.1016\/j.cplett.2020.137354","article-title":"Predicting drug\/phospholipid complexation by the lightGBM method","volume":"747","author":"Gao","year":"2020","journal-title":"Chem Phys Lett"},{"key":"2024011119352495000_ref27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ajps.2014.08.005","article-title":"Nanocrystals for enhancement of oral bioavailability of poorly water-soluble drugs","volume":"10","author":"Junyaprasert","year":"2015","journal-title":"Asian J Pharm Sci"},{"key":"2024011119352495000_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11705-015-1509-3","article-title":"Nanocrystal technology for drug formulation and delivery","volume":"9","author":"Chang","year":"2015","journal-title":"Front Chem Sci Eng"},{"key":"2024011119352495000_ref29","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.jconrel.2020.03.043","article-title":"Can machine learning predict drug nanocrystals?","volume":"322","author":"He","year":"2020","journal-title":"J Control Release Off J Control Release Soc"},{"key":"2024011119352495000_ref30","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.2174\/1567201819666220914113324","article-title":"A comprehensive insight on recent advancements in self-emulsifying drug delivery systems","volume":"20","author":"Kadian","year":"2023","journal-title":"Curr Drug Deliv"},{"key":"2024011119352495000_ref31","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1016\/j.apsb.2021.04.017","article-title":"Integrated in silico formulation design of self-emulsifying drug delivery systems","volume":"11","author":"Gao","year":"2021","journal-title":"Acta Pharm Sin B"},{"key":"2024011119352495000_ref32","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.addr.2020.07.002","article-title":"Liposomes: advancements and innovation in the manufacturing process","volume":"154-155","author":"Shah","year":"2020","journal-title":"Adv Drug Deliv Rev"},{"key":"2024011119352495000_ref33","first-page":"98","article-title":"Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms","volume":"13","author":"Ye","year":"2021","journal-title":"J Chem"},{"key":"2024011119352495000_ref34","first-page":"60","article-title":"ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation","volume":"7","author":"Dong","year":"2015","journal-title":"J Chem"},{"key":"2024011119352495000_ref35","first-page":"16","article-title":"PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions","volume":"10","author":"Dong","year":"2018","journal-title":"J Chem"},{"key":"2024011119352495000_ref36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2024011119352495000_ref37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2024011119352495000_ref38","article-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024011119352495000_ref39","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016","journal-title":"Proc 22nd ACM SIGKDD Int Conf Knowl Discov Data Min"},{"key":"2024011119352495000_ref40","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.3109\/03639048909062752","article-title":"Chemometric modelling of dissolution rates of Griseofulvin from solid dispersions with polymers","volume":"15","author":"Bonelli","year":"1989","journal-title":"Drug Dev Ind Pharm"},{"key":"2024011119352495000_ref41","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.eswa.2004.10.007","article-title":"Neural network as a decision support system in the development of pharmaceutical formulation\u2014focus on solid dispersions","volume":"28","author":"Mendyk","year":"2005","journal-title":"Expert Syst Appl"},{"key":"2024011119352495000_ref42","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ejpb.2012.06.003","article-title":"Optimizing the ability of PVP\/PEG mixtures to be used as appropriate carriers for the preparation of drug solid dispersions by melt mixing technique using artificial neural networks: I","volume":"82","author":"Papadimitriou","year":"2012","journal-title":"Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV"},{"key":"2024011119352495000_ref43","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3109\/03639045.2015.1054831","article-title":"Combined application of mixture experimental design and artificial neural networks in the solid dispersion development","volume":"42","author":"Medarevi\u0107","year":"2016","journal-title":"Drug Dev Ind Pharm"},{"key":"2024011119352495000_ref44","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J Chem Inf Comput Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/1\/bbad419\/55464864\/bbad419.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/1\/bbad419\/55464864\/bbad419.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T19:36:08Z","timestamp":1705001768000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad419\/7441064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,22]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad419","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,1,1]]},"published":{"date-parts":[[2023,11,22]]},"article-number":"bbad419"}}