Deep Learning in Drug Design—Progress, Methods, and Challenges

Seyyed Saeid  Masoomkhah; Khosro  Rezaee; Mojtaba  Ansari; Hossein  Eslami

doi:10.18502/fbt.v11i3.15893

Seyyed Saeid Masoomkhah Department of Biomedical Engineering, Meybod University, Meybod, Iran
Khosro Rezaee Department of Biomedical Engineering, Meybod University, Meybod, Iran
Mojtaba Ansari Department of Biomedical Engineering, Meybod University, Meybod, Iran
Hossein Eslami Department of Biomedical Engineering, Meybod University, Meybod, Iran

DOI: https://doi.org/10.18502/fbt.v11i3.15893

Keywords: Machine Learning; Deep Learning; Drug Design; Drug Discovery; Neural Network

Abstract

Purpose: Artificial Intelligence (AI), which mimics the human brain structure and operation, simulates intelligence. The aim of Machine Learning (ML), which is a branch of artificial intelligence, is to create models by analyzing data. Another type of artificial intelligence, Deep Learning (DL), depicts geometric changes using several layers of model representations. Since DL broke the computational analysis record, AI has advanced in many areas.

Materials and Methods: Contrary to the widespread use of conventional ML methodologies, there is still a need to promote the use and popularity of DL for pharmaceutical research and development. Drug discovery and design have been enhanced by ML and DL in major research projects. To fully realize its potential, drug design must overcome many challenges and issues. Various aspects of medication design must be considered to successfully address these concerns and challenges. This review article explains DL's significance both in technological breakthroughs and in effective medications.

Results: There are numerous barriers and substantial challenges associated with drug design associated with DL architectures and key application domains. The article discusses several elements of medication development that have been influenced by existing research. Two widely used and efficient Neural Network (NN) designs are discussed in this article: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Conclusion: It is described how these tools can be utilized to design and discover small molecules for drug discovery. They are also given an overview of the history of DL approaches, as well as a discussion of some of their drawbacks.