BRDT Inhibitors for Male Contraceptive Drug Discovery: Current Status

2018 ◽  
pp. 287-315 ◽  
Author(s):  
Zhenyuan Miao ◽  
Xianghong Guan ◽  
Jiewei Jiang ◽  
Gunda I. Georg
Keyword(s):  
Drug Discovery ◽  
Current Status ◽  
Male Contraceptive

Dengue Structural Proteins as Antiviral Drug Targets: Current Status in the Drug Discovery & Development

2021 ◽  
pp. 113527
Author(s):  
Akshatha H. S ◽  
Gurubasavaraj V. Pujar ◽  
Arun Kumar Sethu ◽  
Meduri Bhagyalalitha ◽  
Manisha Singh
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Antiviral Drug ◽  
Structural Proteins ◽  
Current Status

scholarly journals Academic drug discovery: current status and prospects

2015 ◽  
Vol 10 (9) ◽  
pp. 937-944 ◽  
Author(s):  
Jeremy R Everett
Keyword(s):  
Drug Discovery ◽  
Current Status

scholarly journals An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1693 ◽  
Author(s):  
Maral Aminpour ◽  
Carlo Montemagno ◽  
Jack A. Tuszynski
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
High Performance ◽  
Current Status ◽  
General Area ◽  
Dna Translocation ◽  
Computer Aided Drug Design ◽  
Major Significance ◽  
In Silico Modeling ◽  
Performance Computing

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

scholarly journals The current status and future directions of hepatitis B antiviral drug discovery

2016 ◽  
Vol 12 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Liudi Tang ◽  
Qiong Zhao ◽  
Shuo Wu ◽  
Junjun Cheng ◽  
Jinhong Chang ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Hepatitis B ◽  
Antiviral Drug ◽  
Current Status ◽  
Future Directions

scholarly journals Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Nadia Terranova ◽  
Karthik Venkatakrishnan ◽  
Lisa J. Benincosa
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Precision Medicine ◽  
Translational Medicine ◽  
Pharmaceutical Research ◽  
Current Status ◽  
Data Sets ◽  
Patient Centered ◽  
Drug Discovery And Development ◽  
Reverse Translation

AbstractThe exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Sign in / Sign up

Export Citation Format

Share Document