Artificial intelligence in drug design
Science China Life Sciences, ISSN: 1674-7305, Vol: 61, Issue: 10, Page: 1191-1204
2018
- 158Citations
- 400Captures
- 4Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations158
- Citation Indexes158
- 158
- CrossRef111
- Captures400
- Readers400
- 400
- Mentions4
- News Mentions4
- News4
Most Recent News
Artificial Intelligence and Tools in Pharmaceuticals: An Overview
Prasad Patil1, Nripesh Kumar Nrip2*, Ashok Hajare3, Digvijay Hajare4, Mahadev K. Patil5, Rajesh Kanthe6, Anil T. Gaikwad7 1,2,6,7 Bharati Vidyapeeth Institute of Management, Kolhapur, Maharashtra,
Review Description
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85050808717&origin=inward; http://dx.doi.org/10.1007/s11427-018-9342-2; http://www.ncbi.nlm.nih.gov/pubmed/30054833; http://link.springer.com/10.1007/s11427-018-9342-2; https://dx.doi.org/10.1007/s11427-018-9342-2; https://link.springer.com/article/10.1007/s11427-018-9342-2; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=6384294&internal_id=6384294&from=elsevier
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