Trends and Advancements of AI and XAI in Drug Discovery

摘要

The rapid advancements in Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) have brought about significant changes in the field of drug discovery. This abstract provides an overview of the emerging trends and progress of AI and XAI in drug discovery, emphasizing their applications and contributions. The utilization of AI techniques, including machine learning algorithms and computational modelling, has revolutionized the drug discovery process. By analysing extensive biological datasets encompassing genomics, proteomics, and chemical structures, AI enables efficient target identification, lead optimization, and prediction of drug properties. AI algorithms offer the ability to identify potential drug targets by analysing intricate biological networks, enabling researchers to prioritize and focus their efforts. Additionally, machine learning algorithms optimize lead compounds by accurately predicting binding affinity, pharmacokinetics, and toxicity, significantly reducing the time and cost associated with traditional experimental approaches. Nevertheless, the interpretability and transparency of AI models have raised concerns, highlighting the importance of XAI in drug discovery. XAI techniques provide valuable insights into the decision-making process of AI models, allowing researchers to understand the underlying mechanisms behind predictions. By enhancing interpretability, XAI instils trust and confidence in AI models while also aiding in the identification of potential biases or limitations in data or algorithms. Recent advancements in AI and XAI have further propelled drug discovery. Deep learning techniques, such as convolutional neural networks and recurrent neural networks, have demonstrated improved accuracy and efficiency in tasks such as virtual screening, de novo drug design, and drug repurposing. Moreover, the integration of AI and XAI with emerging technologies such as high-throughput screening, robotics, and automation has accelerated the drug discovery process. AI-driven robotics enable rapid screening of large compound libraries, while XAI provides valuable insights into the decision-making process of these automated systems. In conclusion, AI and XAI have transformed the landscape of drug discovery. Leveraging AI algorithms and machine learning techniques has expedited target identification and led optimization. XAI methods address the need for transparency and interpretability, fostering trust and aiding in bias identification. Continued advancements in AI and XAI hold immense potential to revolutionize drug discovery, leading to the discovery of novel therapeutics and improved healthcare outcomes.