Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery based on big data to precised, knowledgeable data. The pharmaceutical companies have already geared their departments for this and started a race to search for new novel drugs. The AI helps to predict the molecular structure of the compound and its in-vivo vs. in-vitro characteristics without hampering life, thus saving time and economic loss. Clinical studies, electronic records, and images act as a helping hand for the development. The data mining and curation techniques help explore the data with a single click. AI in big data analysis has paved the red carpet for future rational drug development and optimization. This review's objective is to familiarise readers with various advances in the AI field concerning software, firms, and other tools working in easing out the labor of the drug discovery journey.
Keywords: Artificial intelligence; deep learning; drug discovery; electronic records; high-throughput screening; machine learning; molecular docking.
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