Alzheimer's disease (AD) is a complex neurodegenerative disease that seriously impairs cognitive function and daily activities. Patients with this ailment suffer from symptoms of memory deficit, cognitive deficit, language impairment and changes in personality and behavior. Rising life expectancy globally results in a higher incidence of AD, imposing more and more burdens not only on patients and their families but also on the social healthcare system.
Drug development against AD represents one of the major challenges for humankind. Over the last few years, with the rapid development of artificial intelligence technology, a plethora of studies have emerged that revolve around the question of prediction, diagnosis, and therapy of AD by implementing artificial intelligence technologies such as deep learning and machine learning. In the following, we will cite a review paper where authors discussed the contribution of AI technology to the process of drug discovery and development for AD and its related dementias.
The article summarizes AI-driven approaches to AD drug discovery and development, including:
Target identification: the article provides some emerging AD drug targets and highlights the potential value of these targets in drug discovery. The new AD drug targets mentioned in the article are:
De novo drug design: Designing new drug molecules from scratch using AI algorithms.
Virtual screening: Screening a large number of compounds to identify potential drug candidates through AI technology.
Prediction of drug-target interactions: AI predicts the interactions of drug molecules with disease-related targets.
Drug repurposing: Using AI methods to find new indications for existing drugs.
Fig. 1. A general schematic for applications of AI methods in AD drug discovery. (Qiu Y.; et al. 2024)
With AI-guided drug discovery, combined with genetics and multi-omics analysis (including genomics, epigenomics, transcriptomics, proteomics, and metabolomics), we can better understand pathophysiology and precision medicine. It is stated in the article that 158 AI-driven drug candidates are currently in the discovery or preclinical stage for various diseases.
The article provides some examples of AI-based techniques in the AD drug discovery process to illustrate how AI methods may be used to assist in identifying new drug candidates as well as new targets. For instance, use an Enc-Dec-based architecture that generates models-3D binding pockets where features are acquired from those ligand-protein complexes.
AD drug development is now a priority in global public health. This article throws an accent on the capacity of AI in AD drug discovery and, particularly, repurposing and precision medicine.
Despite the remarkable progress made by AI technology in this field, challenges remain that need to be overcome by further research and innovation. Protheragen-ING AI-Pharma stands at the forefront of AI-guided drug development and design, and we are committed to revolutionizing the drug discovery and development field by providing industry-leading AI platforms and customized services. If you are interested in our services or have a question, please feel free to contact us for more details.
Original Article:
Qiu Y.; et al. (2024). Artificial intelligence for drug discovery and development in Alzheimer's disease. Current Opinion in Structural Biology. 2024, 85: 102776.
Services Related in the Article:
Target Identification
AI-powered Drug Discovery and Design
Drug Repositioning and Repurposing
AI-assisted Drug High Throughput Screening
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