Title:A Review on Artificial Intelligence Approaches and Rational Approaches in Drug
Discovery
Volume: 29
Issue: 15
Author(s): 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*Parasuraman Pavadai*
Affiliation:
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar,
Bengaluru, 560054, India
Keywords:
Artificial intelligence, neural network, drug design, virtual screening, toxicity, recursive neural network.
Abstract: 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.
