Abstract
Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore, new attempts have been made in using computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying the computational method for predicting drug-drug interactions. We first briefly introduce the widely used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions prediction. We also discussed the challenges and opportunities of applying the computational method in drug-drug interactions prediction.
Keywords: Drug, drug-drug interactions, deep learning, machine learning, computational methods, biomedical informatics.
Current Pharmaceutical Design
Title:The Next Generation of Machine Learning in DDIs Prediction
Volume: 27 Issue: 23
Author(s): Wei Huang, Chunyan Li*, Ying Ju and Yan Gao
Affiliation:
- Yunnan Minzu University, Kunming,China
Keywords: Drug, drug-drug interactions, deep learning, machine learning, computational methods, biomedical informatics.
Abstract: Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore, new attempts have been made in using computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying the computational method for predicting drug-drug interactions. We first briefly introduce the widely used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions prediction. We also discussed the challenges and opportunities of applying the computational method in drug-drug interactions prediction.
Export Options
About this article
Cite this article as:
Huang Wei , Li Chunyan *, Ju Ying and Gao Yan , The Next Generation of Machine Learning in DDIs Prediction, Current Pharmaceutical Design 2021; 27 (23) . https://dx.doi.org/10.2174/1381612827666210127122312
DOI https://dx.doi.org/10.2174/1381612827666210127122312 |
Print ISSN 1381-6128 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4286 |
Call for Papers in Thematic Issues
"Tuberculosis Prevention, Diagnosis and Drug Discovery"
The Nobel Prize-winning discoveries of Mycobacterium tuberculosis and streptomycin have enabled an appropriate diagnosis and an effective treatment of tuberculosis (TB). Since then, many newer diagnosis methods and drugs have been saving millions of lives. Despite advances in the past, TB is still a leading cause of infectious disease mortality ...read more
Current Pharmaceutical challenges in the treatment and diagnosis of neurological dysfunctions
Neurological dysfunctions (MND, ALS, MS, PD, AD, HD, ALS, Autism, OCD etc..) present significant challenges in both diagnosis and treatment, often necessitating innovative approaches and therapeutic interventions. This thematic issue aims to explore the current pharmaceutical landscape surrounding neurological disorders, shedding light on the challenges faced by researchers, clinicians, and ...read more
Emerging and re-emerging diseases
Faced with a possible endemic situation of COVID-19, the world has experienced two important phenomena, the emergence of new infectious diseases and/or the resurgence of previously eradicated infectious diseases. Furthermore, the geographic distribution of such diseases has also undergone changes. This context, in turn, may have a strong relationship with ...read more
Melanoma and Non-Melanoma Skin Cancer Treatment: Standard of Care and Recent Advances
In this thematic issue, we aim to provide a standard of care of the diagnosis and treatment of melanoma and non-melanoma skin cancer. The editor will invite authors from different countries who will write review articles of melanoma and non-melanoma skin cancers. The Diagnosis, Staging, Surgical Treatment, Non-Surgical Treatment all ...read more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements