Advanced AI Applications for Drug Discovery

Introduction

The field of drug discovery is an ever-evolving domain marked by continuous advancements and challenges (Cichońska et al., 2024; Yingngam, 2024). This complex process, traditionally encompassing the identification of potential therapeutic targets, validation, and optimization of drug candidates, is fundamental in developing new medical treatments (Qureshi et al., 2023). In recent years, the industry has experienced a surge in the number of drug molecules entering the development pipeline, driven by expanding research and a deeper understanding of diseases at the molecular level (Yoo et al., 2023). However, despite these advancements, the drug discovery process is fraught with high failure rates and escalating costs (Yingngam, 2023). This reflects the inherent difficulty of translating scientific discoveries into viable therapeutic solutions (Pun et al., 2023). Traditional drug discovery methods face numerous challenges. First, the process is time-consuming and expensive, often taking more than a decade and costing billions of dollars to bring a single drug to market (W. Chen, X. Liu, et al., 2023; Kelm et al., 2024; Pyrkov et al., 2023). The high failure rates, particularly in clinical trials, contribute significantly to these costs (Singh et al., 2024). Additionally, the complexity of biological systems and the limited predictability of human responses to new compounds make drug development a high-risk venture (W. Chen, C. Song, et al., 2023). Moreover, the conventional ‘one-size-fits-all’ approach in drug development often overlooks genetic and environmental variations in patient populations, potentially leading to limited efficacy and adverse drug reactions in certain groups (Krentzel et al., 2023; Odunitan et al., 2024). In response to these challenges, the emergence of artificial intelligence (AI) in drug discovery has led to a significant shift in the field (Bentwich, 2023). With its ability to process vast amounts of data and uncover patterns not readily apparent to humans, AI is poised to significantly enhance various aspects of drug discovery (Cerchia & Lavecchia, 2023). Artificial intelligence (AI) algorithms, particularly in machine learning and deep learning, have shown promise in identifying novel drug targets, predicting drug efficacy and toxicity, and reducing the time and cost associated with drug development (Oselusi et al., 2024; Stossi et al., 2023). The integration of AI not only streamlines the drug discovery process but also enables more personalized approaches to therapy (Parvatikar et al., 2023). By leveraging big data and advanced computational techniques, AI is opening new frontiers in developing more effective and safer drugs, thus reshaping the future of pharmaceutical research and healthcare (Lv et al., 2023).

The aims of this chapter are as follows:

• (1)

  To comprehensively examine the evolving landscape of drug discovery, highlighting both the advancements and challenges within traditional methods.

• (2)

  To explore the transformative impact of AI in drug discovery, the authors focus on how AI methodologies such as machine learning and deep learning are reshaping the drug development process.

• (3)

  To detail the applications of AI in critical areas of drug discovery, such as target identification, drug design optimization, and the prediction of drug efficacy and toxicity.

• (4)

  To discuss the ethical, regulatory, and practical challenges associated with the integration of AI in drug discovery, including data security and the implications of machine learning models.

• (5)

  To provide insights into the future direction of AI in drug discovery, the authors speculate on its potential synergies with personalized medicine and genomics and forecast its long-term impact on healthcare and therapeutic development.