New Drug Design Avenues Targeting Alzheimer’s Disease by Pharmacoinformatics-Aided Tools

Abstract

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Synergistic Approaches in Neurodegenerative Therapeutics: Multi-Target Drug Innovative Interventions for Alzheimer’s Disease

“…Similarly, researchers at the University of Arizona College of Medicine used AI to explore the molecular changes in healthy neurons during the progression of AD, identifying complex pathways involved in the disease. This AI and big data-driven approach highlights the potential for developing innovative treatments by targeting newly identified or combination pathways in AD [114]. Additionally, a Bayesian ML model utilizing data from ChEMBL and PubChem databases aimed to identify a novel small molecule with therapeutic potential for Alzheimerʹs, leading to the identification of GSK3β as a promising target [115].…”

Receptor-based approaches and therapeutic targets in Alzheimer’s disease along with role of AI in drug designing: Unraveling pathologies and advancing treatment strategies

“…AI algorithms can analyze large-scale drug databases and clinical data to identify approved drugs or investigational compounds that may have therapeutic potential for AD. This approach accelerates the drug development process by repurposing existing drugs, which have already undergone safety testing and may require less time and resources for further evaluation (Table 10) [128] .…”

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

“…We can employ computational methods as an essential option to overcome the limits of the screening process. 10 , 11 …”

“…Negative screening results, on the other hand, may become less important during testing. We can employ computational methods as an essential option to overcome the limits of the screening process. , …”

“…We can employ computational methods as an essential option to overcome the limits of the screening process. 10,11 Furthermore, the computational method enables both timeand cost-effective solutions. Virtual screening procedures can be a viable option.…”