Guest Editors
Dr. Faisal Saeed, Birmingham City University, UK
Dr. Shadi Basurra, Birmingham City University, UK
Dr. Jawad Ahmad, Edinburgh Napier University, UK
Dr. Mohammed Al-Sarem, Taibah University, Saudi Arabia
Dr. Maged Nasser, Universiti Teknologi Malaysia, Malaysia
Summary
Computational methods have been widely applied to get insights from several huge chemical databases and used in several pharmaceutical applications. To conduct an effective comparative molecular similarity analysis, the Ligand-based virtual screening (LBVS) methods are applied to molecules with known and unknown activities.
Machine learning applications have been recently adopted for virtual screening in order to prioritize molecular databases as active against a particular protein target and used to predict the bioactivity of molecules. To work with these huge databases, several pre-processing and modelling methods are needed, such as data preparation and information retrieval.
The application of intelligent systems based on artificial intelligence, machine learning, deep learning and big data offers significant advantages and can make important contributions to the drug discovery process, particular in its early phase (lead identification process), where a large number of molecules are screened and sought to discover the novel actives.
Both LBVS and similarity searching have been enhanced by machine learning approaches to solve many issues in pharmaceutical applications and increasing the rapidity of screening. Several studies highlighted that intelligent systems such as artificial intelligence and deep learning had contributed significantly to this field. Therefore, new smart methods and protocols can be developed to enhance and outperform the existing and established techniques. In addition, data fusion approaches allow the combination of different methods to find more robust outcomes in chemoinformatics applications.
Keywords
• Molecular Informatics
• Ligand-based drug design
• Sentiment Analysis in Drug Review
• Health Informatics
• Chemical Information Retrieval
• Machine Learning for Pharmaceutical and Healthcare Systems
• Deep Learning for Pharmaceutical and Healthcare Systems
• Big Data Analytics for Pharmaceutical and Healthcare Systems
• Artificial Intelligence Pharmaceutical and Healthcare Systems
• Chemoinformatics
• Bioinformatics
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