Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations

Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.

A theoretical screening of phytochemical constituents from Millettia brandisiana as inhibitors against acetylcholinesterase

Alzheimer's disease is one of the causes associated with the early stages of dementia. Nowadays, the main treatment available is to inhibit the actions of the acetylcholinesterase (AChE) enzyme, which has been identified as responsible for the disease. In this study, computational methods were used to examine the structure and therapeutic ability of chemical compounds extracted from Millettia brandisiana natural products against AChE. This plant is commonly known as a traditional medicine in Vietnam and Thailand for the treatment of several diseases. Furthermore, machine learning helped us narrow down the choice of 85 substances for further studies by molecular docking and molecular dynamics simulations to gain deeper insights into the interactions between inhibitors and disease proteins. Of the five top-choice substances, γ-dimethylallyloxy-5,7,2,5-tetramethoxyisoflavone emerges as a promising substance due to its large free binding energy to AChE and the high thermodynamic stability of the resulting complex.

Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Two new iridoids and triterpenoid analogues from the leaves of Viburnum chingii and their anti-acetylcholinesterase activity

Two undescribed split-ring iridoids (1-2) with six known triterpenes (3-8) and one steride (9) were isolated from the Viburnum chingii. Compound 2 possessed an unprecedented split-ring iridoid skeleton formed by electrocyclic reaction and split ring. The structures and absolute configurations of the new iridoids were established by NMR, HRESIMS, and ECD calculations. All the isolated compounds were tested for AChE inhibitory activity. Biologically, 1, 2, 3, 4, and 7 displayed significant AChE effects compared to the positive control donepezil, and have also been subjected to molecular docking studies.

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC₅₀ value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC₅₀s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at megaache.collaborationspharma.com.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Probing the Reactions of Thiourea (CH4N2S) with Metals (X = Au, Hf, Hg, Ir, Os, W, Pt, and Re) Anchored on Fullerene Surfaces (C59X)

Upon various investigations conducted in search for a nanosensor material with the best sensing performance, the need to explore these materials cannot be overemphasized as materials associated with best sensing attributes are of vast interest to researchers. Hence, there is a need to investigate the adsorption performances of various metal-doped fullerene surfaces: C59Au, C59Hf, C59Hg, C59Ir, C59Os, C59Pt, C59Re, and C59W on thiourea [SC(NH2)2] molecule using first-principles density functional theory computation. Comparative adsorption study has been carried out on various adsorption models of four functionals, M06-2X, M062X-D3, PBE0-D3, and ωB97XD, and two double-hybrid (DH) functionals, DSDPBEP86 and PBE0DH, as reference at Gen/def2svp/LanL2DZ. The visual study of weak interactions such as quantum theory of atoms in molecule analysis and noncovalent interaction analysis has been invoked to ascertain these results, and hence we arrived at a conclusive scientific report. In all cases, the weak adsorption observed is best described as physisorption phenomena, and CH4N2S@C59Pt complex exhibits better sensing attributes than its studied counterparts in the interactions between thiourea molecule and transition metal-doped fullerene surfaces. Also, in the comparative adsorption study, DH density functionals show better performance in estimating the adsorption energies due to their reduced mean absolute deviation (MAD) and root-mean-square deviation (RMSD) values of (MAD = 1.0305, RMSD = 1.6277) and (MAD = 0.9965, RMSD = 1.6101) in DSDPBEP86 and PBE0DH, respectively.