Trends in Pharmacological Sciences, Journal Year: 2021, Volume and Issue: 42(9), P. 745 - 757
Published: July 5, 2021
Language: Английский
Trends in Pharmacological Sciences, Journal Year: 2021, Volume and Issue: 42(9), P. 745 - 757
Published: July 5, 2021
Language: Английский
Journal of Controlled Release, Journal Year: 2020, Volume and Issue: 329, P. 743 - 757
Published: Oct. 5, 2020
Language: Английский
Citations
302Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 178, P. 113958 - 113958
Published: Sept. 1, 2021
Language: Английский
Citations
277Journal of Controlled Release, Journal Year: 2021, Volume and Issue: 332, P. 367 - 389
Published: Feb. 27, 2021
Language: Английский
Citations
252Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 174, P. 553 - 575
Published: May 20, 2021
Language: Английский
Citations
216Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 174, P. 406 - 424
Published: May 2, 2021
Language: Английский
Citations
183Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 173, P. 349 - 373
Published: April 6, 2021
Additive manufacturing (AM) is gaining interests in drug delivery applications, offering innovative opportunities for the design and development of systems with complex geometry programmed controlled release profile. In addition, polymer-based can improve safety, efficacy, patient compliance, are key materials AM. Therefore, combining AM polymers be beneficial to overcome existing limitations systems. Considering these advantages, here we focusing on recent developments field polymeric prepared by This review provides a comprehensive overview holistic polymer–AM perspective discussion materials, properties, fabrication techniques mechanisms used achieve system. The current challenges future perspectives personalized medicine clinical use also briefly discussed.
Language: Английский
Citations
166Drug Discovery Today, Journal Year: 2020, Volume and Issue: 26(3), P. 769 - 777
Published: Dec. 5, 2020
The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do limitations become apparent. Such include need for big data, sparsity in and lack interpretability. It has also apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail advanced circumvent these challenges, with examples drawn from allied disciplines. addition, present emerging potential role discovery. presented herein anticipated expand applicability ML
Language: Английский
Citations
146Advanced Drug Delivery Reviews, Journal Year: 2022, Volume and Issue: 182, P. 114098 - 114098
Published: Jan. 5, 2022
Language: Английский
Citations
133Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 175, P. 113805 - 113805
Published: May 18, 2021
Language: Английский
Citations
132Advanced Drug Delivery Reviews, Journal Year: 2021, Volume and Issue: 175, P. 113810 - 113810
Published: May 23, 2021
Language: Английский
Citations
132