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Review Article

A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery

[ Vol. 29 , Issue. 15 ]


Anjana Vidya Srivathsa, Nandini Markuli Sadashivappa, Apeksha Krishnamurthy Hegde, Srimathi Radha, Agasa Ramu Mahesh, Damodar Nayak Ammunje, Debanjan Sen, Panneerselvam Theivendren, Saravanan Govindaraj, Selvaraj Kunjiappan* and Parasuraman Pavadai*   Pages 1180 - 1192 ( 13 )


Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain’s thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.


Artificial intelligence, neural network, drug design, virtual screening, toxicity, recursive neural network.


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