QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Identification of Drug Compounds for Capsular Contracture Based on Text Mining and Deep Learning

BACKGROUND

Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear, and the effectiveness of nonsurgical treatment is still doubtful. The authors' study aimed to investigate new drug therapies for capsular contracture by using computational methods.

METHODS

Genes related to capsular contracture were identified by text mining and GeneCodis. Then, the candidate key genes were selected through protein-protein interaction analysis in Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. Drugs targeting the candidate genes with relation to capsular contracture were screened out in Pharmaprojects. Based on the drug-target interaction analysis by DeepPurpose, candidate drugs with highest predicted binding affinity were obtained eventually.

RESULTS

The authors' study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated eight candidate genes. One hundred drugs targeting the candidate genes were selected. The seven candidate drugs with the highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha antagonist, estrogen receptor agonist, insulin-like growth factor 1 receptor, tyrosine kinase inhibitor, and matrix metallopeptidase 1 inhibitor.

CONCLUSION

Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring nonsurgical treatment to capsular contracture.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, V.

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

Advances of computer-aided drug design (CADD) in the development of anti-Azheimer's-disease drugs

Alzheimer's disease (AD) is a degenerative disease of the nervous system that progressively destroys memory and thinking skills. Currently there is no treatment to prevent or cure AD; targeting the direct cause of neuronal degeneration would constitute a rational strategy and hopefully offer better options for the treatment of AD. This paper first summarizes the physiological and pathological pathogenesis of AD and then discusses the representative drug candidates for targeted therapy of AD and their binding mode with their targets. Finally, the applications of computer-aided drug design in discovering anti-AD drugs are reviewed. Teaser.

Combining machine learning and structure-based approaches to develop oncogene PIM kinase inhibitors

Introduction: PIM kinases are targets for therapeutic intervention since they are associated with a number of malignancies by boosting cell survival and proliferation. Over the past years, the rate of new PIM inhibitors discovery has increased significantly, however, new generation of potent molecules with the right pharmacologic profiles were in demand that can probably lead to the development of Pim kinase inhibitors that are effective against human cancer.

Method: In the current study, a machine learning and structure based approaches were used to generate novel and effective chemical therapeutics for PIM-1 kinase. Four different machine learning methods, namely, support vector machine, random forest, k-nearest neighbour and XGBoost have been used for the development of models. Total, 54 Descriptors have been selected using the Boruta method.

Results: SVM, Random Forest and XGBoost shows better performance as compared to k-NN. An ensemble approach was implemented and, finally, four potential molecules (CHEMBL303779, CHEMBL690270, MHC07198, and CHEMBL748285) were found to be effective for the modulation of PIM-1 activity. Molecular docking and molecular dynamic simulation corroborated the potentiality of the selected molecules. The molecular dynamics (MD) simulation study indicated the stability between protein and ligands.

Discussion: Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery against PIM kinase.

A parametric approach for molecular encodings using multilevel atomic neighborhoods applied to peptide classification

Exploring new ways to represent and discover organic molecules is critical to the development of new therapies. Fingerprinting algorithms are used to encode or machine-read organic molecules. Molecular encodings facilitate the computation of distance and similarity measurements to support tasks such as similarity search or virtual screening. Motivated by the ubiquity of carbon and the emerging structured patterns, we propose a parametric approach for molecular encodings using carbon-based multilevel atomic neighborhoods. It implements a walk along the carbon chain of a molecule to compute different representations of the neighborhoods in the form of a binary or numerical array that can later be exported into an image. Applied to the task of binary peptide classification, the evaluation was performed by using forty-nine encodings of twenty-nine data sets from various biomedical fields, resulting in well over 1421 machine learning models. By design, the parametric approach is domain- and task-agnostic and scopes all organic molecules including unnatural and exotic amino acids as well as cyclic peptides. Applied to peptide classification, our results point to a number of promising applications and extensions. The parametric approach was developed as a Python package (cmangoes), the source code and documentation of which can be found at https://github.com/ghattab/cmangoes and https://doi.org/10.5281/zenodo.7483771.

mibPOPdb: An online database for microbial biodegradation of persistent organic pollutants

Microbial biodegradation of persistent organic pollutants (POPs) is an attractive, ecofriendly, and cost-efficient clean-up technique for reclaiming POP-contaminated environments. In the last few decades, the number of publications documenting POP-degrading microbes, enzymes, and experimental data sets has continuously increased, necessitating the development of a dedicated web resource that catalogs consolidated information on POP-degrading microbes and tools to facilitate integrative analysis of POP degradation data sets. To address this knowledge gap, we developed the Microbial Biodegradation of Persistent Organic Pollutants Database (mibPOPdb) by accumulating microbial POP degradation information from the public domain and manually curating published scientific literature. Currently, in mibPOPdb, there are 9215 microbial strain entries, including 184 gene (sub)families, 100 enzymes, 48 biodegradation pathways, and 593 intermediate compounds identified in POP-biodegradation processes, and information on 32 toxic compounds listed under the Stockholm Convention environmental treaty. Besides the standard database functionalities, which include data searching, browsing, and retrieval of database entries, we provide a suite of bioinformatics services to facilitate comparative analysis of users' own data sets against mibPOPdb entries. Additionally, we built a Graph Neural Network-based prediction model for the biodegradability classification of chemicals. The predictive model exhibited a good biodegradability classification performance and high prediction accuracy. mibPOPdb is a free data-sharing platform designated to promote research in microbial-based biodegradation of POPs and fills a long-standing gap in environmental protection research. Database URL: http://mibpop.genome-mining.cn/.

Functionalized Graphene Oxide with Chitosan for Dopamine Biosensing

Detecting biological structures via a rapid and facile method has become a pronounced point of research. Dopamine (DA) detection is critical for the early diagnosis of a variety of neurological diseases/disorders. A study on the real-time optical detection of DA is described here using graphene oxide (GO) functionalized with chitosan (Cs). Hence, a computational model dependent on a high theoretical level density functional theory (DFT) using the B3LYP/LANL2DZ model is carried out to study the physical as well as electronic properties of the proposed interaction between GO functionalized with Cs and its interaction with DA. GO functionalized with a Cs biopolymer was verified as having much higher stability and reactivity. Moreover, the addition of DA to functionalized GO yields structures with the same stability and reactivity. This ensures that GO-Cs is a stable structure with a strong interaction with DA, which is energetically preferred. Molecular electrostatic potential (MESP) calculation maps indicated that the impact of an interaction between GO and Cs increases the number of electron clouds at the terminals, ensuring the great ability of this composite when interacting with DA. Hence, these calculations and experimental results support the feasibility of using GO functionalized with Cs as a DA biosensor.

Integrated Machine Learning and Chemoinformatics-Based Screening of Mycotic Compounds against Kinesin Spindle ProteinEg5 for Lung Cancer Therapy

Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (-)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer.

QSAR Modeling, Molecular Docking and Cytotoxic Evaluation for Novel Oxidovanadium(IV) Complexes as Colon Anticancer Agents

Four new drug-based oxidovanadium (IV) complexes were synthesized and characterized by various spectral techniques, including molar conductance, magnetic measurements, and thermogravimetric analysis. Moreover, optimal structures geometry for all syntheses was obtained by the Gaussian09 program via the DFT/B3LYP method and showed that all of the metal complexes adopted a square-pyramidal structure. The essential parameters, electrophilicity (ω) value and expression for the maximum charge that an electrophile molecule may accept (ΔN_max) showed the practical biological potency of [VO(CTZ)₂] 2H₂O. The complexes were also evaluated for their propensity to bind to DNA through UV–vis absorption titration. The result revealed a high binding ability of the [VO(CTZ)₂] 2H₂O complex with K_b = 1.40 × 10⁶ M⁻¹. Furthermore, molecular docking was carried out to study the behavior of the VO (II) complexes towards colon cancer cell (3IG7) protein. A quantitative structure–activity relationship (QSAR) study was also implemented for the newly synthesized compounds. The results of validation indicate that the generated QSAR model possessed a high predictive power (R² = 0.97). Within the investigated series, the [VO(CTZ)₂] 2H₂O complex showed the greatest potential the most selective compound comparing to the stander chemotherapy drug.

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secretase complex, leads to the production and release of amyloid-β peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-β in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.

Structure-based design and classifications of small molecules regulating the circadian rhythm period

Circadian rhythm is an important mechanism that controls behavior and biochemical events based on 24 h rhythmicity. Ample evidence indicates disturbance of this mechanism is associated with different diseases such as cancer, mood disorders, and familial delayed phase sleep disorder. Therefore, drug discovery studies have been initiated using high throughput screening. Recently the crystal structures of core clock proteins (CLOCK/BMAL1, Cryptochromes (CRY), Periods), responsible for generating circadian rhythm, have been solved. Availability of structures makes amenable core clock proteins to design molecules regulating their activity by using in silico approaches. In addition to that, the implementation of classification features of molecules based on their toxicity and activity will improve the accuracy of the drug discovery process. Here, we identified 171 molecules that target functional domains of a core clock protein, CRY1, using structure-based drug design methods. We experimentally determined that 115 molecules were nontoxic, and 21 molecules significantly lengthened the period of circadian rhythm in U2OS cells. We then performed a machine learning study to classify these molecules for identifying features that make them toxic and lengthen the circadian period. Decision tree classifiers (DTC) identified 13 molecular descriptors, which predict the toxicity of molecules with a mean accuracy of 79.53% using tenfold cross-validation. Gradient boosting classifiers (XGBC) identified 10 molecular descriptors that predict and increase in the circadian period length with a mean accuracy of 86.56% with tenfold cross-validation. Our results suggested that these features can be used in QSAR studies to design novel nontoxic molecules that exhibit period lengthening activity.

Simulating drug concentrations in PDMS microfluidic organ chips

Microfluidic organ-on-a-chip (Organ Chip) cell culture devices are often fabricated using polydimethylsiloxane (PDMS) because it is biocompatible, transparent, elastomeric, and oxygen permeable; however, hydrophobic small molecules can absorb to PDMS, which makes it challenging to predict drug responses. Here, we describe a combined simulation and experimental approach to predict the spatial and temporal concentration profile of a drug under continuous dosing in a PDMS Organ Chip containing two parallel channels separated by a porous membrane that is lined with cultured cells, without prior knowledge of its log P value. First, a three-dimensional finite element model of drug loss into the chip was developed that incorporates absorption, adsorption, convection, and diffusion, which simulates changes in drug levels over time and space as a function of potential PDMS diffusion coefficients and log P values. By then experimentally measuring the diffusivity of the compound in PDMS and determining its partition coefficient through mass spectrometric analysis of the drug concentration in the channel outflow, it is possible to estimate the effective log P range of the compound. The diffusion and partition coefficients were experimentally derived for the antimalarial drug and potential SARS-CoV-2 therapeutic, amodiaquine, and incorporated into the model to quantitatively estimate the drug-specific concentration profile over time measured in human lung airway chips lined with bronchial epithelium interfaced with pulmonary microvascular endothelium. The same strategy can be applied to any device geometry, surface treatment, or in vitro microfluidic model to simulate the spatial and temporal gradient of a drug in 3D without prior knowledge of the partition coefficient or the rate of diffusion in PDMS. Thus, this approach may expand the use of PDMS Organ Chip devices for various forms of drug testing.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R² = 0.8688, q² = 0.8600) and BACE1 (R² = 0.8177, q² = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r² = 0.8469, r²_pred = 0.8138) and BACE1 (r² = 0.7805, r²_pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations

Sirtuin 2 (Sirt2) nicotinamide adenine dinucleotide-dependent deacetylase enzyme has been reported to alter diverse biological functions in the cells and onset of diseases, including cancer, aging, and neurodegenerative diseases, which implicate the regulation of Sirt2 function as a potential drug target. Available Sirt2 inhibitors or modulators exhibit insufficient specificity and potency, and even partially contradictory Sirt2 effects were described for the available inhibitors. Herein, we applied computational screening and evaluation of FDA-approved drugs for highly selective modulation of Sirt2 activity via a unique inhibitory mechanism as reported earlier for SirReal2 inhibitor. Application of stringent molecular docking results in the identification of 48 FDA-approved drugs as selective putative inhibitors of Sirt2, but only top 10 drugs with docking scores > - 11 kcal/mol were considered in reference to SirReal2 inhibitor for computational analysis. The molecular dynamics simulations and post-simulation analysis of Sirt2-drug complexes revealed substantial stability for Fluphenazine and Nintedanib with Sirt2. Additionally, developed 3D-QSAR-models also support the inhibitory potential of drugs, which exclusively revealed highest activities for Nintedanib (pIC50 ≥ 5.90 µM). Conclusively, screened FDA-approved drugs were advocated as promising agents for Sirt2 inhibition and required in vitro investigation for Sirt2 targeted drug development.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.

Neighborhood degree sum-based molecular descriptors of fractal and Cayley tree dendrimers

Topological index is a connection between the chemical structure and the real number that remains invariant under graph isomorphism. In structure-property and structure-activity modeling, topological indices are considered as essential molecular descriptors to predict different physicochemical properties of molecule. Dendrimers are considered to be the most significant, commercially accessible basic components in nanotechnology. In this report, some neighborhood degree sum-based molecular descriptors are obtained for the fractal tree and the Cayley tree dendrimers. Neighborhood M-polynomial yields a family of topological indices for a molecular graph in less time compared to the usual computation from their definitions. Some indices are obtained using neighborhood M-polynomial approach. In addition, some multiplicative neighborhood degree sum-based molecular descriptors are evaluated for fractal and Cayley tree dendrimers. The graphical representations of the outcomes are presented. A comparative study of the findings with some well-known degree-based indices is performed. Usefulness of the descriptors in modeling different properties and activities is discussed.

Scope of β-Secretase (BACE1)-Targeted Therapy in Alzheimer's Disease: Emphasizing the Flavonoid Based Natural Scaffold for BACE1 Inhibition

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia in the world. Studies report the presence of extracellular amyloid plaques consisting of β-amyloid peptide and intracellular tangles consisting of hyperphosphorylated tau proteins as the histopathological indicators of AD. The process of β-amyloid peptide generation by sequential cleavage of amyloid precursor protein by β-secretase (BACE1) and γ-secretase, followed by its aggregation to form amyloid plaques, is the mechanistic basis of the amyloid hypothesis. Other popular hypotheses related to the pathogenesis of AD include the tau hypothesis and the oxidative stress hypothesis. Various targets of the amyloid cascade are now in prime focus to develop drugs for AD. Many BACE1 inhibitors, β-amyloid aggregation inhibitors, and Aβ clearance strategies using monoclonal antibodies are in various stages of clinical trials. This review provides an in-depth evaluation of the role of BACE1 in disease pathogenesis and also highlights the therapeutic approaches developed to find more potent but less toxic inhibitors for BACE1, particularly emphasizing the natural scaffold as a nontoxic lead for BACE1 inhibition. Cellular targets and signaling cascades involving BACE1 have been highlighted to understand the physiological role of BACE1. This knowledge is extremely crucial to understand the toxicity evaluations for BACE1-targeted therapy. We have particularly highlighted the scope of flavonoids as a new generation of nontoxic BACE1 inhibitory scaffolds. The structure-activity relationship of BACE1 inhibition for this group of compounds has been highlighted to provide a guideline to design more selective highly potent inhibitors. The review aims to provide a holistic overview of BACE1-targeted therapy for AD that paves the way for future drug development.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

An Analysis of QSAR Research Based on Machine Learning Concepts

Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

Novel small molecule therapeutic agents for Alzheimer disease: Focusing on BACE1 and multi-target directed ligands

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that effects 50 million people worldwide. In this review, AD pathology and the development of novel therapeutic agents targeting AD were fully discussed. In particular, common approaches to prevent Aβ production and/or accumulation in the brain including α-secretase activators, specific γ-secretase modulators and small molecules BACE1 inhibitors were reviewed. Additionally, natural-origin bioactive compounds that provide AD therapeutic advances have been introduced. Considering AD is a multifactorial disease, the therapeutic potential of diverse multi target-directed ligands (MTDLs) that combine the efficacy of cholinesterase (ChE) inhibitors, MAO (monoamine oxidase) inhibitors, BACE1 inhibitors, phosphodiesterase 4D (PDE4D) inhibitors, for the treatment of AD are also reviewed. This article also highlights descriptions on the regulator of serotonin receptor (5-HT), metal chelators, anti-aggregants, antioxidants and neuroprotective agents targeting AD. Finally, current computational methods for evaluating the structure-activity relationships (SAR) and virtual screening (VS) of AD drugs are discussed and evaluated.

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

Peptidomimetic Modulators of BACE1

The β-site APP Cleaving enzyme 1 (BACE1) is a membrane-associated aspartyl protease which mediates the production of amyloid-β (Aβ), a major component of amyloid plaques in the Alzheimer’s disease brain. We have synthesised and characterised a series of peptidomimetic analogues of BACE substrates that incorporate two distinct stabilising structures. To demonstrate the potential activity of these compounds, a variety of assaying strategies were used to investigate cleavage susceptibility and inhibition potency under competitive and non-competitive conditions. β-Amino acids and scissile site N-methylation were incorporated into peptide substrate templates as transition state isostere (TSI) substitutes by positional scanning to generate series of non-TSI β-peptidomimetics. The amino acid sequences flanking the β-cleavage site within APP carrying the Swedish double mutation (APPSW), Neuregulin, the synthetic hydroxyethylene-based TSI peptide inhibitor OM99-2, and the high affinity peptide sequence SEISYEVEFR, served as the four substrate templates from which over 60 peptides were designed and synthesised by solid phase peptide synthesis. A quenched fluorescent substrate BACE1 assay in conjunction with liquid chromatography–mass spectrometry (LC-MS) analysis was established to investigate cleavage susceptibility and inhibition potency under competitive and non-competitive conditions. It was determined that β-amino acids substituted at the P1 scissile site position within known peptide substrates were resistant to proteolysis, and particular substitutions induced a concentration-dependent stimulation of BACE1, indicating a possible modulatory role of native BACE1 substrates.

Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.