Comprehensive Organic Name Reactions and Reagents. 3 Bände. Von Zerong Wang.

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides

A new series of benzylaminochalcone derivatives with different substituents on ring B were synthesized and evaluated as inhibitors of acetylcholinesterase. The study is aimed at identification of novel benzylaminochalcones capable of blocking acetylcholinesterase activity for further development of an approach to Alzheimer’s disease treatment. These compounds were produced in moderate to good yields via Claisen-Schmidt condensation and subjected to an in vitro acetylcholinesterase inhibition assay, using Ellman’s method. The in silico docking procedure was also employed to identify molecular interactions between the chalcone compounds and the enzyme. Compounds with ring B bearing pyridin-4-yl, 4-nitrophenyl, 4-chlorophenyl and 3,4-dimethoxyphenyl moieties were discovered to exhibit significant inhibitory activities against acetylcholinesterase, with IC50 values ranging from 23 to 39 µM. The molecular modeling studies are consistent with the hypothesis that benzylaminochalcones could exert their effects as dual-binding-site acetylcholinesterase inhibitors, which might simultaneously enhance cholinergic neurotransmission and inhibit β-amyloid aggregation through binding to both catalytic and peripheral sites of the enzyme. These derivatives could be further developed to provide novel leads for the discovery of new anti-Alzheimer drugs in the future.

Synthesis of Novel Chalcones as Acetylcholinesterase Inhibitors

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r2) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti.

Heterocycles are the key structures in organic chemistry owing to their immense applications in the biological, chemical, and pharmaceutical fields. Heterocyclic compounds perform various noteworthy functions in nature, medication, innovation etc. Most frequently, pure nitrogen heterocycles or various positional combinations of nitrogen, oxygen, and sulfur atoms in five or six-membered rings can be found. Inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes is a popular strategy for the management of numerous mental diseases. In this context, cholinesterase inhibitors are utilized to relieve the symptoms of neurological illnesses like dementia and Alzheimer's disease (AD). The present review focuses on various heterocyclic scaffolds and their role in designing and developing new potential AChE and BChE inhibitors to treat AD. Moreover, a detailed structure–activity relationship (SAR) has been established for the future discovery of novel drugs for the treatment of AD. Most of the heterocyclic motifs have been used in the design of new potent cholinesterase inhibitors. In this regard, this review is an endeavor to summarize the biological and chemical studies over the past decade (2010–2022) describing the pursuit of new N, O and S containing heterocycles which can offer a rich supply of promising AChE and BChE inhibitory activities.

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Inhibitory potential of nitrogen, oxygen and sulfur containing heterocyclic scaffolds against acetylcholinesterase and butyrylcholinesterase

Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are the two crucial enzymes involved in the pathology of Alzheimer's disease. The former is responsible for many defects in cholinergic signaling pathway and the latter is the primary enzyme in the biosynthesis of beta-amyloid as the main component of the amyloid plaques. These both abnormalities are found in the brains of Alzheimer's patients. In this study, in silico models were developed, including 3D-pharmacophore, 2D-QSAR (two-dimensional quantitative structure-activity relationship), and molecular docking, to screen virtually a database of compounds for AChE and BACE-1 inhibitory activities. A combinatorial library containing more than 3 million structures of curcumin and flavonoid derivatives was generated and screened for drug-likeness and enzymatic inhibitory bioactivities against AChE and BACE-1 through the validated in silico models. A total of 47 substances (two curcumins and 45 flavonoids), with remarkable predicted pIC50 values against AChE and BACE-1 ranging from 4.24-5.11 (AChE) and 4.52-10.27 (BACE-1), were designed. The in vitro assays on AChE and BACE-1 were performed and confirmed the in silico results. The study indicated that, by using in silico methods, a series of curcumin and flavonoid structures were generated with promising predicted bioactivities. This would be a helpful foundation for the experimental investigations in the future. Designed compounds which were the most feasible for chemical synthesis could be potential candidates for further research and lead optimization.

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Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches.

Acetylcholinesterase (AChE) and β-secretase (BACE-1) have become attractive therapeutic targets for Alzheimer's disease (AD). Flavones are flavonoid derivatives with various bioactive effects, including AChE and BACE-1 inhibition. In the present work, a series of 14 flavone derivatives was synthesized in relatively high yields (35-85%). Six of the synthetic flavones (B4, B5, B6, B8, D6 and D7) had completely new structures. The AChE and BACE-1 inhibitory activities were tested, giving pIC50 3.47-4.59 (AChE) and 4.15-5.80 (BACE-1). Three compounds (B3, D5 and D6) exhibited the highest biological effects on both AChE and BACE-1. A molecular docking investigation was conducted to explain the experimental results. These molecules could be employed for further studies to discover new structures with dual action on both AChE and BACE-1 that could serve as novel therapies for AD.

https://www.mdpi.com/1420-3049/25/18/4064/pdf

Synthesis, In Silico and In Vitro Evaluation of Some Flavone Derivatives for Acetylcholinesterase and BACE-1 Inhibitory Activity

P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approach in the fight against cancer. Therefore, the discovery of P-gp inhibitors is considered one of the most popular strategies to reverse MDR in tumour cells and to improve therapeutic efficacy of commonly used cytotoxic drugs. Until now, several generations of P-gp inhibitors have been developed but they have largely failed in preclinical and clinical studies due to lack of selectivity, poor solubility and severe pharmacokinetic interactions. In this study, three models (SION, SIO, SIN) to classify specific 'true' P-gp inhibitors as well as three other models (CPBN, CPB1, CPN) to distinguish between P-gp inhibitors, CYP 3A inhibitors and co-inhibitors of these proteins with rather high accuracy values for the test set and the external set were generated based on counter-propagation neural networks (CPG-NN). Such three and four-class classification models helped provide more information about the bioactivities of compounds not only on one target (P-gp), but also on a combination of multiple targets (P-gp, CYP 3A).

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are two attractive targets in the discovery of novel substances that could control multiple aspects of Alzheimer's disease (AD). Chalcones are the flavonoid derivatives with diverse bioactivities, including AChE and BACE-1 inhibition. In this study, a series of N-substituted-4-phenothiazine-chalcones was synthesized and tested for AChE and BACE-1 inhibitory activities. In silico models, including two-dimensional quantitative structure-activity relationship (2D-QSAR) for AChE and BACE-1 inhibitors, and molecular docking investigation, were developed to elucidate the experimental process. The results indicated that 13 chalcone derivatives were synthesized with relatively high yields (39-81%). The bioactivities of these substances were examined with pIC50 3.73-5.96 (AChE) and 5.20-6.81 (BACE-1). Eleven of synthesized chalcones had completely new structures. Two substances AC4 and AC12 exhibited the highest biological activities on both AChE and BACE-1. These substances could be employed for further researches. In addition to this, the present study results suggested that, by using a combination of two types of predictive models, 2D-QSAR and molecular docking, it was possible to estimate the biological activities of the prepared compounds with relatively high accuracy.

https://www.mdpi.com/1420-3049/25/17/3916/pdf

Synthesis, In Silico and In Vitro Evaluation for Acetylcholinesterase and BACE-1 Inhibitory Activity of Some N-Substituted-4-Phenothiazine-Chalcones.

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Thai-Son Tran

Papers

Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches.

Synthesis, In Silico and In Vitro Evaluation of Some Flavone Derivatives for Acetylcholinesterase and BACE-1 Inhibitory Activity

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

Synthesis, In Silico and In Vitro Evaluation for Acetylcholinesterase and BACE-1 Inhibitory Activity of Some N-Substituted-4-Phenothiazine-Chalcones.

Synthesis and Evaluation of the Acetylcholinesterase Inhibitory Activities of Some Flavonoids Derived from Naringenin