Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction

Malani, Manisha; Hiremath, Manthan S.; Sharma, Surbhi; Jhunjhunwala, Manisha; Gayen, Shovanlal; Hota, Chittaranjan; Nirmal, Jayabalan

doi:10.1080/07391102.2023.2226717

Journal of Biomolecular Structure and Dynamics

2023

DOI: 10.1080/07391102.2023.2226717

|View full text |Cite|

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction

Manisha Malani,

Manthan S. Hiremath,

Surbhi Sharma

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine Learning Techniques Applied to the Study of Drug Transporters

Kong

Lin

et al. 2023

Molecules

View full text Add to dashboard Cite

With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug–drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.

show abstract

Machine Learning Techniques Applied to the Study of Drug Transporters

Kong

Lin

et al. 2023

Molecules

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction

Cited by 1 publication

References 62 publications

Machine Learning Techniques Applied to the Study of Drug Transporters

Machine Learning Techniques Applied to the Study of Drug Transporters

Contact Info

Product

Resources

About