Alzheimer's disease drug development based on Computer-Aided Drug Design

Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis

Background

Understanding how artificial intelligence (AI) is employed to predict, diagnose, and perform relevant analyses in Alzheimer's disease research is a rapidly evolving field. This study integrated and analyzed the relevant literature from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) on the application of AI in Alzheimer's disease (AD), covering publications from 2004 to 2023.

Objective

This study aims to identify the key research hotspots and trends of the application of AI in AD over the past 20 years through a bibliometric analysis.

Methods

Using the Web of Science Core Collection database, we conducted a comprehensive visual analysis of literature on AI and AD published between January 1, 2004, and December 31, 2023. The study utilized Excel, Scimago Graphica, VOSviewer, and CiteSpace software to visualize trends in annual publications and the distribution of research by countries, institutions, journals, references, authors, and keywords related to this topic.

Results

A total of 2,316 papers were obtained through the research process, with a significant increase in publications observed since 2018, signaling notable growth in this field. The United States, China, and the United Kingdom made notable contributions to this research area. The University of London led in institutional productivity with 80 publications, followed by the University of California System with 74 publications. Regarding total publications, the Journal of Alzheimer's Disease was the most prolific while Neuroimage ranked as the most cited journal. Shen Dinggang was the top author in both total publications and average citations. Analysis of reference and keyword highlighted research hotspots, including the identification of various stages of AD, early diagnostic screening, risk prediction, and prediction of disease progression. The "task analysis" keyword emerged as a research frontier from 2021 to 2023.

Conclusion

Research on AI applications in AD holds significant potential for practical advancements, attracting increasing attention from scholars. Deep learning (DL) techniques have emerged as a key research focus for AD diagnosis. Future research will explore AI methods, particularly task analysis, emphasizing integrating multimodal data and utilizing deep neural networks. These approaches aim to identify emerging risk factors, such as environmental influences on AD onset, predict disease progression with high accuracy, and support the development of prevention strategies. Ultimately, AI-driven innovations will transform AD management from a progressive, incurable state to a more manageable and potentially reversible condition, thereby improving healthcare, rehabilitation, and long-term care solutions.

Chromone-based monoamine oxidase B inhibitor with potential iron-chelating activity for the treatment of Alzheimer's disease

Based on the multitarget-directed ligands (MTDLs) strategy, a series of chromone-hydroxypyridinone hybrids were designed, synthesised, and evaluated as potential multimodal anti-AD ligands. Prospective iron-chelating effects and favourable monoamine oxidase B (MAO-B) inhibitory activities were observed for most of the compounds. Pharmacological assays led to the identification of compound 17d, which exhibited favourable iron-chelating potential (pFe³⁺ = 18.52) and selective hMAO-B inhibitory activity (IC₅₀ = 67.02 ± 4.3 nM, SI = 11). Docking simulation showed that 17d occupied both the substrate and the entrance cavity of MAO-B, and established several key interactions with the pocket residues. Moreover, 17d was determined to cross the blood-brain barrier (BBB), and can significantly ameliorate scopolamine-induced cognitive impairment in AD mice. Despite its undesired pharmacokinetic property, 17d remains a promising multifaceted agent that is worth further investigation.

Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer's Disease

Natural products are compounds isolated from plants that provide a variety of lead structures for the development of new drugs by the pharmaceutical industry. The interest in these substances increases because of their beneficial effects on human health. Alzheimer's disease (AD) affects occur in about 80% of individuals aged 65 years. AD, the most common cause of dementia in elderly people, is characterized by progressive neurodegenerative alterations, as decrease of cholinergic impulse, increased toxic effects caused by reactive oxygen species and the inflammatory process that the amyloid plaque participates. In silico studies is relevant in the process of drug discovery; through technological advances in the areas of structural characterization of molecules, computational science and molecular biology have contributed to the planning of new drugs used against neurodegenerative diseases. Considering the social impairment caused by an increased incidence of disease and that there is no chemotherapy treatment effective against AD; several compounds are studied. In the researches for effective neuroprotectants as potential treatments for Alzheimer's disease, natural products have been extensively studied in various AD models. This study aims to carry out a literature review with articles that address the in silico studies of natural products aimed at potential drugs against Alzheimer's disease (AD) in the period from 2015 to 2021.

Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery

Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.

A review of the mechanisms underlying selected comorbidities in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the central nervous system (CNS) leading to mental deterioration and devastation, and eventually a fatal outcome. AD affects mostly the elderly. AD is frequently accompanied by hypercholesterolemia, hypertension, atherosclerosis, and diabetes mellitus, and these are significant risk factors of AD. Other conditions triggered by the progression of AD include psychosis, sleep disorders, epilepsy, and depression. One important comorbidity is Down’s syndrome, which directly contributes to the severity and rapid progression of AD. The development of new therapeutic strategies for AD includes the repurposing of drugs currently used for the treatment of comorbidities. A better understanding of the influence of comorbidities on the pathogenesis of AD, and the medications used in its treatment, might allow better control of disease progression, and more effective pharmacotherapy.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

BACE1 Inhibitor, Neuroprotective, and Neuritogenic Activities of Melatonin Derivatives

Alzheimer’s disease (AD) is a common chronic neurodegenerative disorders. Melatonin (MLT) has been reported to be neuroprotective agent, and its modified structures exhibit potent antioxidant and anti-inflammation activities. Therefore, the activity of MLT and its derivatives against AD was investigated. Herein, the targeted enzymes, such as β-secretase (BACE1) and acetylcholinesterase (AChE), as well as the neuroprotective and neuritogenic effects on P19-derived neurons were evaluated. All the derivatives (1–5), including MLT, displayed potent inhibitory activity for BACE1, with inhibition values of more than 75% at 5 µM. A molecular docking study predicted that MLT, 5-MT, and 5 bound with BACE1 at catalytic amino acids Asp32 and the flap region, whereas 1–4 interacted with allosteric residue Thr232 and the flap region. The additional π-π interactions between 2, 3, and 5 with Tyr71 promoted ligand-enzyme binding. In addition, MLT, 1, 3, and 5 significantly protected neuron cells from oxidative stress by increasing the cell viability to 97.95, 74.29, 70.80, and 69.50% at 1 nM, respectively. Moreover, these derivatives significantly induced neurite outgrowth by increasing the neurite length and number. The derivatives 1, 3, and 5 should be thoroughly studied as potential AD treatment and neuroprotective agents.

Beta-naphthoflavone inhibits LPS-induced inflammation in BV-2 cells via AKT/Nrf-2/HO-1-NF-κB signaling axis

Numerous studies have shown that over-activation of microglia could cause neuroinflammation and release pro-inflammatory mediators, which could result in neurodegenerative diseases, like Parkinson's disease, Alzheimer's disease etc. Beta-naphthoflavone (BNF) has anti-oxidant and anti-inflammatory effects in borderline tissues, but BNF has not been reported the effect associated with neuroinflammation. Therefore, the purpose of this experiment is to inquiry the impact and mechanism of BNF on neuroinflammation. The results indicated that BNF significantly inhibited the production of pro-inflammatory mediators (inducible nitric-oxide synthase (iNOS), Cyclooxygenase-2 (COX-2), tumor necrosis factor-α (TNF-α) andinterleukin-6 (IL-6)) in LPS-exposed BV-2 cells. Analysis of western blot results found that BNF accelerated the activation of AKT/Nrf-2/HO-1 signaling pathway and suppressed NF-κB pathway activation. Further study showed that BNF inhibited activation of NF-κB pathway via promoting HO-1, and SnPP IX (a HO-1 inhibitor) could inhibit anti-inflammatory function of BNF. We also found that BNF reduced the apoptosis rate of Human neuroblastoma cells (SHSY5Y) and mouse hippocampal neuron cell line (HT22) by inhibiting release of inflammatory mediators in LPS-exposed BV2 cells. In a word, our results suggested that BNF could inhibit inflammatory response via AKT/Nrf-2/HO-1-NF-κB signaling axis in BV-2 cells and exerts neuroprotective impact via inhibiting the activation of BV2 cells.

Proposition of Potential GSK-3β Inhibitors for the Treatment of Alzheimer's Disease: A Molecular Modeling Study

INTRODUCTION

The enzyme Glycogen Synthase Kinase 3-β (GSK-3β) is related to neuronal cell degeneration, representing a promising target to treat Alzheimer's Disease (AD).

METHODS

In this work, we performed a molecular modeling study of existing GSK-3β inhibitors by means of evaluation of their IC50 values, derivation of a pharmacophore model, molecular docking simulations, ADME/Tox properties predictions, molecular modifications and prediction of synthetic viability.

RESULTS

In this manner, inhibitor 15 (CID 57399952) was elected a template molecule, since it demonstrated to bear relevant structural groups able to interact with GSK-3β, and also presented favorable ADME/Tox predicted properties, except for mutagenicity. Based on this inhibitor chemical structure we proposed six analogues that presented the absence of alerts for mutagenic and carcinogenic activity, both for rats and mouse; likewise they all presented low risk alerts for inhibition of hERG and medium prediction of synthetic viability.

CONCLUSION

It is concluded that the analogues of GSK-3β inhibitors were optimized in relation to the toxicity endpoint of the template molecule, being, therefore, presented as novel and promising drug candidates for AD treatment.

Schizophrenia and Corollary Discharge: A Neuroscientific Overview and Translational Implications

Corollary discharge mechanism refers to the suppression of sensory consequences of self-generated actions; a process that serves to distinguish between self and non-self based on discrimination of origination of action. It explains, say for example, why we cannot tickle ourselves. This review discusses how corollary discharge model is an essential neural integration mechanism central to the motor functioning of animal kingdom. In this article, research conducted in the field of corollary discharge has been reviewed to understand the neuroanatomical and neurophysiological basis of corollary discharge and gain insight into the biochemical basis of its dysfunction. This review article also explores the role of corollary discharge and its dysfunction in the presentation of symptoms of schizophrenia, discussing the findings from corollary discharge studies on schizophrenia population. Lastly, the link between schizophrenia psychopathology and corollary discharge dysfunction has been highlighted, and an attempt has been made to establish a case for correction of corollary discharge deficit in schizophrenia through neuromodulation.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11-40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

In Vitro and In Silico Acetylcholinesterase Inhibitory Activity of Thalictricavine and Canadine and Their Predicted Penetration across the Blood-Brain Barrier

In recent studies, several alkaloids acting as cholinesterase inhibitors were isolated from Corydalis cava (Papaveraceae). Inhibitory activities of (+)-thalictricavine (1) and (+)-canadine (2) on human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBChE) were evaluated with the Ellman's spectrophotometric method. Molecular modeling was used to inspect the binding mode of compounds into the active site pocket of hAChE. The possible permeability of 1 and 2 through the blood⁻brain barrier (BBB) was predicted by the parallel artificial permeation assay (PAMPA) and logBB calculation. In vitro, 1 and 2 were found to be selective hAChE inhibitors with IC50 values of 0.38 ± 0.05 µM and 0.70 ± 0.07 µM, respectively, but against hBChE were considered inactive (IC50 values > 100 µM). Furthermore, both alkaloids demonstrated a competitive-type pattern of hAChE inhibition and bind, most probably, in the same AChE sub-site as its substrate. In silico docking experiments allowed us to confirm their binding poses into the active center of hAChE. Based on the PAMPA and logBB calculation, 2 is potentially centrally active, but for 1 BBB crossing is limited. In conclusion, 1 and 2 appear as potential lead compounds for the treatment of Alzheimer's disease.

Hybrid approach to sieve out natural compounds against dual targets in Alzheimer's Disease

Excess Aβ production by the key protease BACE1, results in Aβ aggregation, forming amyloid plaques, all of which contribute to the pathogenesis of Alzheimer’s disease. Besides the multi-factorial nature of the disease, the diversity in the size and shape of known ligands that bind to the active site of BACE1, that is the flexibility of the enzyme, pose a serious challenge for the identification of drug candidates. To address the issue of receptor flexibility we have carried out ensemble docking with multiple receptor conformations. Therein, two representative structures each from closed and semi-open BACE1 conformations were selected for virtual screening to identify compounds that bind to the active site of both the conformations. These outperformed compounds were ranked using pharmacophore models generated by a ligand-based approach, for the identification of BACE1 inhibitors. The inhibitors were further predicted for anti-amyloidogenic activity using a QSAR model already established by our group thus enlisting compounds with dual potency. BACE1 inhibitory and anti-amyloidogenic activity for the commercially available compounds were validated using in vitro studies. Thus, incorporation of receptor flexibility in BACE1 through ensemble docking in conjunction with structure and ligand-based approach for screening might act as an effective protocol for obtaining promising scaffolds against AD.

De-novo Drug Design, Molecular Docking and In-Silico Molecular Prediction of AChEI Analogues through CADD Approaches as Anti-Alzheimer's Agents

Background: There are over 44 million persons who suffer with Alzheimer’s disease (AD) worldwide, no existence of cure and only symptomatic treatments are available for it. The aim of this study is to evaluate the anti-Alzheimer potential of designed AChEI analogues using computer simulation docking studies. AChEIs are the most potential standards for treatment of AD, because they have proven efficacy. Among all AChEIs donepezil possesses lowest adverse effects, it can treat mild-moderate-severe AD and only once-daily dosing is required. Therefore, donepezil is recognized as a significant prototype for design and development of new drug molecule.

Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8

Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.

Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders

Background

Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity.

Methods

A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review.

Results

In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed.

Conclusion

CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs.

MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors

Alzheimer's disease is a common form of dementia, which considered to be a major health concern. Multivariate Image Analysis - Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of unstudied compounds based on 2D image analysis. This study focuses on constructing an efficient QSAR model using a dataset of 52 flavonoid derivatives (substituted with amino-alkyl, alkoxy, alkyl-amines, and piperidine groups) as active compounds against acetylcholinesterase inhibitors (AChE). The model was constructed by PLS (Partial Least Square) using NIPALS (Non-Linear iterative Partial Least Square) algorithm. The comparable values obtained from calibration of training set using five latent variables (R² = 0.955) and external validation of test set (Q² = 0.948) confirmed the precision in the prediction of bioactivities for the set of flavonoid derivatives used in designing the model.

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s

When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue damage occurs. Current Food and Drug Administration approved treatments include supplemental recombinant C1 inhibitor, but these are extremely costly and a more economical solution is desired. In our work, we have utilized an existing data set of 136 compounds that have been previously tested for activity against C1. Using these compounds and the activity data, we have created models using principal component analysis, genetic algorithm, and support vector machine approaches to characterize activity. The models were then utilized to virtually screen the 72 million compound PubChem repository. This first round of virtual high-throughput screening identified many economical and promising inhibitor candidates, a subset of which was tested to validate their biological activity. These results were used to retrain the models and rescreen PubChem in a second round vHTS. Hit rates for the first round vHTS were 57%, while hit rates for the second round vHTS were 50%. Additional structure⁻property analysis was performed on the active and inactive compounds to identify interesting scaffolds for further investigation.

Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs". Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

A PM7 dynamic residue-ligand interactions energy landscape of the BACE1 inhibitory pathway by hydroxyethylamine compounds. Part I: The flap closure process

BACE1 is an enzyme of scientific interest because it participates in the progression of Alzheimer's disease. Hydroxyethylamines (HEAs) are a family of compounds which exhibit inhibitory activity toward BACE1 at a nanomolar level, favorable pharmacokinetic properties and oral bioavailability. The first step in the inhibition of BACE1 by HEAs consists of their entrance into the protease active site and the resultant conformational change in the protein, from Apo to closed form. These two conformations differ in the position of an antiparallel loop (called the flap) which covers the entrance to the catalytic site. For BACE1, closure of this flap is vital to its catalytic activity and to inhibition of the enzyme due to the new interactions thereby formed with the ligand. In the present study a dynamic energy landscape of residue-ligand interaction energies (ReLIE) measured for 112 amino acids in the BACE1 active site and its immediate vicinity during the closure of the flap induced by 8 HEAs of different inhibitory power is presented. A total of 6.272 million ReLIE calculations, based on the PM7 semiempirical method, provided a deep and quantitative view of the first step in the inhibition of the aspartyl protease. The information suggests that residues Asp93, Asp289, Thr292, Thr293, Asn294 and Arg296 are anchor points for the ligand, accounting for approximately 45% of the total protein-ligand interaction. Additionally, flap closure improved the BACE1-HEA interaction by around 25%. Furthermore, the inhibitory activity of HEAs could be related to the capacity of these ligands to form said anchor point interactions and maintain them over time: the lack of some of these anchor interactions delayed flap closure or impeded it completely, or even caused the flap to reopen. The methodology employed here could be used as a tool to evaluate future structural modifications which lead to improvements in the favorability and stability of BACE1-HEA ReLIEs, aiding in the design of better inhibitors.

2-Substituted-thio-N-(4-substituted-thiazol/1H-imidazol-2-yl)acetamides as BACE1 inhibitors: Synthesis, biological evaluation and docking studies

In this work, a series of 2-substituted-thio-N-(4-substituted-thiazol/1H-imidazol-2-yl)acetamide derivatives were developed as β-secretase (BACE-1) inhibitors. Supported by docking study, a small library of derivatives were designed, synthesized and biologically evaluated in vitro. In addition, the selected compounds were tested with affinity (K_D) towards BACE-1, blood brain barrier (BBB) permeability and cytotoxicity. The studies revealed that the most potent analog 41 (IC₅₀ = 4.6 μM) with high predicted BBB permeability and low cellular cytotoxicity, could serve as a good lead structure for further optimization.