INTERNATIONAL CONGRESS OF
PharmacoGenetics
Harnessing Artificial Intelligence for Innovative Drug Discovery
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Fereshteh Arefi,1,*
1. Biology Department, Faculty of Biosciences, Tehran North Branch, Islamic Azad University, Tehran, Iran
- Introduction: The process of drug development is complex and lengthy, as the main aim is to create treatments for various diseases. The introduction of Artificial Intelligence (AI) in this industry has played a significant role by accelerating the rate of discovery and minimizing costs. The goal of this review is to present some key AI applications in drug discovery, such as Machine Learning (ML) for drug property prediction and molecular optimization, Deep Learning (DL) for searching biological databases and drug retrieval, Natural Language Processing (NLP) for analyzing bioliterature, Generative Models (GM) for molecule design, and networks aimed at drug targeting. The history of AI application in medicine began with expert systems and image recognition and has evolved to include the management of personalized medicine and drug discovery. Despite AI being conceptualized in the 1920s and its first practical realization in mechanical robots, its application in pharmaceutical research and development has grown significantly. Deep learning algorithms, such as artificial neural networks (ANN), are also employed for predicting drug properties and managing information, including potential large datasets and understanding progress in macromolecule drug development. However, there is still room for improvement in these areas.
- Methods: This review explores the application of artificial intelligence (AI) in various stages of drug discovery and development (DDD). The DDD process encompasses target discovery, target validation, lead generation and refinement, and preclinical development. AI techniques, including machine learning (ML) and deep learning (DL), play a crucial role throughout these stages. Key methods highlighted include virtual screening, where AI tools analyze protein structures to predict drug interactions; toxicological profiling, which uses AI for predicting adverse effects; and scoring protein-ligand interactions. Specific AI approaches, such as Kronecker regularized least squares (KronRLS) and SimBoost, are employed to estimate drug-target binding affinities. Additionally, generative models and neural networks contribute to the design of novel drug molecules and the prediction of physicochemical properties. Network-based analyses and natural language processing (NLP) are also discussed for their roles in identifying potential drug targets and repurposing opportunities. The review emphasizes the transformative impact of AI on drug discovery, enhancing efficiency, accuracy, and the ability to navigate complex biological systems. The integration of AI-driven methodologies is positioned as crucial for advancing pharmaceutical research and optimizing drug development processes.
- Results: AI-assisted drug discovery faces several challenges despite its transformative potential. A primary issue is the quality and volume of data; AI models need large, high-quality datasets to avoid biased or inaccurate predictions. In materials science, obtaining comprehensive data is difficult due to the vast variety of materials and properties. Additionally, ensuring accurate data representation and selecting appropriate algorithms for material discovery are critical, as is integrating domain-specific knowledge to interpret AI predictions. Other challenges include the need for diverse and representative training data to prevent model biases and the requirement for substantial computational resources for high-throughput simulations. Validation of AI predictions through experimental testing is time-consuming and costly. Ethical considerations, such as data security and compliance with regulations, also play a crucial role in the implementation of AI. For successful AI integration in drug discovery, interdisciplinary collaboration, improved data management, and ethical practices are essential. Addressing these issues will enhance AI's ability to drive innovation and efficiency in drug discovery processes.
- Conclusion: The future of AI in the field of drug discovery is on the brink of improving automation, which could shift the process from human-assisted to self-sufficient. This transition aims to simplify the drug discovery process by empowering AI to manage the creation, testing, and analysis of new compounds independently. The goal is to establish fully automated laboratories capable of efficiently progressing through the drug discovery cycle. While this offers the potential for faster and more effective drug development, challenges like ensuring the trustworthiness and consistency of AI results persist. Moreover, the success of AI in drug discovery hinges on the availability of top-notch datasets and ongoing investments in AI technology. Despite these obstacles, AI has already demonstrated significant promise in enhancing drug discovery by pinpointing new drug combinations, refining formulations, improving target identification, and enhancing virtual screening procedures. Looking forward, advancements in AI-driven automation are anticipated to bring about a significant transformation in the field, promising a brighter future for more efficient and cost-effective drug discovery.
- Keywords: drug discovery, machine learning, deep learning, artificial intelligence, pharmaceutical AI
