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For: Bannigan P, Aldeghi M, Bao Z, Häse F, Aspuru-Guzik A, Allen C. Machine learning directed drug formulation development. Adv Drug Deliv Rev 2021;175:113806. [PMID: 34019959 DOI: 10.1016/j.addr.2021.05.016] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 17.0] [Reference Citation Analysis]
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6 Jaganathan K, Tayara H, Chong KT. An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors. Pharmaceutics 2022;14:832. [DOI: 10.3390/pharmaceutics14040832] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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8 Awad A, Trenfield SJ, Pollard TD, Ong JJ, Elbadawi M, McCoubrey LE, Goyanes A, Gaisford S, Basit AW. Connected healthcare: Improving patient care using digital health technologies. Adv Drug Deliv Rev 2021;178:113958. [PMID: 34478781 DOI: 10.1016/j.addr.2021.113958] [Cited by in Crossref: 17] [Cited by in F6Publishing: 9] [Article Influence: 17.0] [Reference Citation Analysis]
9 Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022;4:799341. [DOI: 10.3389/fdgth.2022.799341] [Reference Citation Analysis]
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11 Mazzolari A, Scaccabarozzi A, Vistoli G, Pedretti A. MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database. Molecules 2021;26:5857. [PMID: 34641400 DOI: 10.3390/molecules26195857] [Reference Citation Analysis]
12 Muñiz Castro B, Elbadawi M, Ong JJ, Pollard T, Song Z, Gaisford S, Pérez G, Basit AW, Cabalar P, Goyanes A. Machine learning predicts 3D printing performance of over 900 drug delivery systems. J Control Release 2021;337:530-45. [PMID: 34339755 DOI: 10.1016/j.jconrel.2021.07.046] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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