Artificial neural networks (ANNs) are also known as a digitalized model of the mammalian brain and are used in pattern reorganization, including machine translation and speech reorganization. In addition, some attempts have been made to implement it in new drug design or discovery. Presently, the drug development process utilizes the complex approach in identifying a single lead hit which often fails multiple times due to its poor pharmacokinetic properties and severe side effects. This could be the outcome of poor screening approaches for hit candidates and neglecting the probable bias. However, ANNs can be helpful in decision-making for many researchers, as well as in clinical application. Also, for medicinal chemists, it may act as an effective tool to pick lead hit and predict the 3D protein confirmation to evaluate the effectiveness of the selected lead hit. The present study briefs on ANNs that can be used as a predictive tool to classify diseases, in vitro and in vivo data correlation, target identification, absorption, distribution, metabolism, excretion, and toxicity profiling, and its application in modern drug discovery.
Key words: Artificial neural network, Deep learning, Drug discovery, In silico, Machine learning
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