Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

Abstract

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.

Keywords: Artificial intelligence; Autoencoders; Big data; Deep learning; Drug discovery; Machine learning.

Fig. 1

Growth of machine learning with the subsequent increase in big data and computation power; KB—Kilobyte, MB—Megabyte, CPU—Central processing unit, GPU—Graphics processing unit, HTS—High throughput sequencing

Fig. 2

Workflow of machine learning (ML) process in drug discovery

Fig. 3

a Deep learning neural network (DLNN) without dropout b Deep learning neural network (DLNN) with dropout

Fig. 4

De novo chemical design using generative adversarial networks (GANs)

Fig. 5

Representation of an autoencoder. The green circles represent the hidden layer

Fig. 6

A deep autoencoder with hidden layers. The hierarchical representations from the hidden layers can be used as features in the training of learning algorithms

Fig. 7

Schematic representation of stacking ensemble approach

Fig. 8

Role of AI technology in different phases of drug discovery