The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and
natural language processing via recurrent neural networks (RNNs). However, success …

This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …

Advancement in the field of computational research has made it possible for the in silico
methods to offer significant benefits to both regulatory needs and requirements for risk …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …

Highlights•Deep learning technology has gained remarkable success.•We highlight the
recent applications of deep learning in drug discovery research.•Some popular deep …

Big data and machine learning are having an impact on most aspects of modern life, from
entertainment, commerce, and healthcare. Netflix knows which films and series people …