Bridging Bytes and Biology-Advanced Learning and Bioinformatics in Innovative Drug Discovery

Bridging Bytes and Biology-Advanced Learning and Bioinformatics in Innovative Drug Discovery

DOI: 10.4018/979-8-3693-1822-5.ch006
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Abstract

In this chapter, the authors explore the synergistic integration of advanced machine learning techniques with bioinformatics to revolutionize drug discovery. This interdisciplinary research is jointed on the premise that combining computational power with biological data can lead to more efficient, precise, and innovative approaches to identifying new therapeutic compounds. The chapter begins by detailing the journey of drug discovery from ancient times to modern. The authors discuss the current challenges in drug discovery and delve into how machine learning algorithms can analyze vast amounts of biological data. A significant portion of the chapter is dedicated to case studies where these techniques have been successfully applied, demonstrating their potential in identifying novel drug candidates and in personalizing treatment. The authors also discuss the various regulations and guidelines that address the challenges of data heterogeneity and the need for robust and interpretable models.
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1. Introduction

The history of drug creation is an interesting journey that shows how science and medicine have changed over time. In the past, most drug research was based on empirical methods, which were based on the knowledge of traditional healers who used plants, minerals, and animal parts to treat a wide range of illnesses. This method was based on trying things out and learning from cures that have been passed down from generation to generation. Researchers began to look into the natural world in a more organized way during the Renaissance. This made it easier to find drugs like morphine. The 19th and 20th centuries were the start of modern medicines, as shown in Tables 1 and 2. During this time, big steps forward were made in the creation of drugs, like making aspirin and finding antibiotics like penicillin. The last few years of the 20th century and the start of the 21st have seen the rise of biotechnology and the use of genetic and molecular biology techniques. It made it easier for focused medicines and personalized medicines to get better. Drug development is a difficult and multidisciplinary process that brings together chemistry, biology, computer science, and engineering to create new medicines for a wide range of illnesses.

Table 1.
The progression of drug discovery
Timeline:Key Innovations:
Ancient TimesHerbal remedies and traditional medicinal practices
18th-19th CenturyIsolation of active ingredients from natural products
Early 20th CenturyDevelopment of synthetic drugs (e.g., Aspirin)
Mid-20th CenturyDiscovery of antibiotics and large-scale drug manufacturing
Late 20th CenturyAdvancements in molecular biology and genetic engineering
21st CenturyIntegration of bioinformatics and personalized medicine
Table 2.
The progression of drug discovery in 21st century
Timeline:Key Innovations:
2000s - Genomics Era BeginsCompletion of the Human Genome Project, leading to a better understanding of genetic factors in diseases.
2010s - Rise of Personalized MedicineDevelopment of therapies tailored to individual genetic profiles, targeting specific disease mechanisms.
2010s - Advancements in BiologicsIncreased development and approval of biologic drugs, including monoclonal antibodies, for various diseases.
2020s - AI and Machine Learning IntegrationApplication of AI and ML in drug discovery and development, enhancing efficiency and predictive accuracy.
2020s - CRISPR Gene EditingCRISPR technology revolutionizing gene editing, with potential for treating genetic disorders.

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