Applications of Artificial Intelligence to Drug Design and Discovery in the Big Data Era

Dear Colleagues,

Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation, leading to new computational gateways to address these issues. Variations in genomic data such as SNPs, insertions and deletions, structural variants, and copy number variations in the human genome play a distinctive role in the manifestation and progression of diseases such as cancer, diabetes, and neurodegenerative and cardiovascular diseases. Hence, biomarkers are being investigated as a way of predicting certain diseases and to identify patient subgroups that respond only to specific drugs. Because of huge progress in high-performance computing, and the accumulation of enormous biomedical and pharmacological data, computer-assisted drug design methods are playing a key role in drug discovery and drug repurposing (repositioning) due to their speed and low cost. Recently, with the development of deep learning methods, we have observed transformational changes in biomedical research. The aim of this Special Issue is to cover various aspects of recent progress in the applications of artificial intelligence in biomarker and drug discovery.

Communications, full papers, and reviews on the abovementioned topics are particularly welcome.

Prof. Dr. Juan Luis Fernández-Martínez
Prof. Dr. Andrzej Kloczkowski
Guest Editors

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