Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells.

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm - multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79-0.80, Q2 LOO = 0.78-0.79, Q2 LMO = 0.78-0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = -7.91 kcal/mol) and scaffold 5 (Docking Score = -8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

Anticancer activity of THMPP: Downregulation of PI3K/ S6K1 in breast cancer cell line.

Breast cancer is the most common cancer that majorly affects female. The present study is focused on exploring the potential anticancer activity of 2-((1, 2, 3, 4-Tetrahydroquinolin-1-yl) (4 methoxyphenyl) methyl) phenol (THMPP), against human breast cancer. The mechanism of action, activation of specific signaling pathways, structural activity relationship and drug-likeness properties of THMPP remains elusive. Cell proliferation and viability assay, caspase enzyme activity, DNA fragmentation and FITC/Annexin V, AO/EtBr staining, RT-PCR, QSAR and ADME analysis were executed to understand the mode of action of the drug. The effect of THMPP on multiple breast cancer cell lines (MCF-7 and SkBr3), and non-tumorigenic cell line (H9C2) was assessed by MTT assay. THMPP at IC50 concentration of 83.23 µM and 113.94 µM, induced cell death in MCF-7 and SkBr3 cells, respectively. Increased level of caspase-3 and -9, fragmentation of DNA, translocation of phosphatidylserine membrane and morphological changes in the cells confirmed the effect of THMPP in inducing the apoptosis. Gene expression analysis has shown that THMPP was able to downregulate the expression of PI3K/S6K1 genes, possibly via EGFR signaling pathway in both the cell lines, MCF-7 and SkBr3. Further, molecular docking also confirms the potential binding of THMPP with EGFR. QSAR and ADME analysis proved THMPP as an effective anti-breast cancer drug, exhibiting important pharmacological properties. Overall, the results suggest that THMPP induced cell death might be regulated by EGFR signaling pathway which augments THMPP being developed as a potential candidate for treating breast cancer.

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches.

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach.

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances.

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.

Predicting the potential toxicity of 26 components in Cassiae semen using in silico and in vitro approaches.

Cassiae semen are dried and ripe seeds of Cassia obtusifolia L. or Cassia tora L. (Fabaceae) and have been made into roasted tea or used as a traditional medicine in Asian countries. However, it was reported to result in liver and renal toxicity. The components of Cassiae semen that induce hepatotoxicity or nephrotoxicity remain unknown. In the present study, we evaluate the potential toxicity of 26 newly isolated compounds from Cassiae semen using quantitative structure-activity relationship (QSAR) methods and co-culture of hepatic and renal cell approaches, and we aim to illustrate the relationship between the structural characteristics and cytotoxicity by general linear models (GLMs). Both the QSAR models and co-culture of hepatic and renal cell systems predicted that 6 compounds were potentially hepatotoxic, 10 compounds were potentially nephrotoxic, and specific anthraquinones and anthraquinone-glucosides were potential toxicants in Cassiae semen. Specific groups such as -OH and -OCH3 at the R1, R2, R3, and R7 positions influenced the cytotoxicity.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands.

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

A review on machine learning approaches and trends in drug discovery.

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Predicting PBT and CMR properties of substances of very high concern (SVHCs) using QSAR models, and application for K-REACH.

Quantitative structure-activity relationship (QSAR) models have been applied to predict a variety of toxicity endpoints. Their performance needs to be validated, in a variety of cases, to increase their applicability to chemical regulation. Using the data set of substances of very high concern (SVHCs), the performance of QSAR models were evaluated to predict the persistence and bioaccumulation of PBT, and the carcinogenicity and mutagenicity of CMR. BIOWIN and Toxtree showed higher sensitivity than other QSAR models - the former for persistence and bioaccumulation, the latter for carcinogenicity. In terms of mutagenicity, the sensitivities of QSAR models were underestimated, Toxtree was more accurate and specific than lazy structure-activity relationships (LAZARs) and Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR). Using the weight of evidence (WoE) approach, which integrates results of individual QSAR models, enhanced the sensitivity of each toxicity endpoint. On the basis of obtained results, in particular the prediction of persistence and bioaccumulation by KOWWIN, a conservative criterion is recommended of log Kow greater than 4.5 in K-REACH, without an upper limit. This study suggests that reliable production of toxicity data by QSAR models is facilitated by a better understanding of the performance of these models.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

An overview on medicinal perspective of thiazolidine-2,4-dione: A remarkable scaffold in the treatment of type 2 diabetes.

Diabetes or diabetes mellitus is a complex or polygenic disorder, which is characterized by increased levels of glucose (hyperglycemia) and deficiency in insulin secretion or resistance to insulin over an elongated period in the liver and peripheral tissues. Thiazolidine-2,4-dione (TZD) is a privileged scaffold and an outstanding heterocyclic moiety in the field of drug discovery, which provides various opportunities in exploring this moiety as an antidiabetic agent. In the past few years, various novel synthetic approaches had been undertaken to synthesize different derivatives to explore them as more potent antidiabetic agents with devoid of side effects (i.e., edema, weight gain, and bladder cancer) of clinically used TZD (pioglitazone and rosiglitazone). In this review, an effort has been made to summarize the up to date research work of various synthetic strategies for TZD derivatives as well as their biological significance and clinical studies of TZDs in combination with other category as antidiabetic agents. This review also highlights the structure-activity relationships and the molecular docking studies to convey the interaction of various synthesized novel derivatives with its receptor site.

An Energetically Favorable Ligand Entrance Gate of a Multidrug Transporter Revealed by Partial Nudged Elastic Band Simulations.

P-glycoprotein (P-gp) is a multidrug transporter, which harnesses the chemical energy of ATP to power the efflux of diverse chemotherapeutics out of cells and thus contributes to the development of multidrug resistance (MDR) in cancer. It has been proved that the ligand-binding pocket of P-gp is located at the transmembrane domains (TMDs). However, the access of ligands into the binding pocket remains to be elucidated, which definitely hinder the development of P-gp inhibitors. Herein, the access pathways of a well-known substrate rhodamine-123 and a cyclopeptide inhibitor QZ-Leu were characterized by time-independent partial nudged elastic band (PNEB) simulations. The decreasing free energies along the PNEB-optimized access pathway indicated that TM4/6 cleft may be an energetically favorable entrance gate for ligand entry into the binding pocket of P-gp. The results can be reconciled with a range of experimental studies, further corroborating the reliability of the gate revealed by computational simulations. Our atomic level description of the ligand access pathway provides valuable insights into the gating mechanism for drug uptake and transport by P-gp and other multidrug transporters.

In silico approaches to evaluate the molecular properties of organophosphate compounds to inhibit acetylcholinesterase activity in housefly.

Organophosphate compounds (OPC) have become the primary choice as insecticides and are widely used across the world. Additionally, OPCs were also commonly used as a chemical warfare agent that triggers a great challenge to public safety. Exposure of OPCs to human causes immediate excitation of cholinergic neurotransmission through transient elevation of synaptic acetylcholine (ACh) levels and accumulations. Likewise, prolonged exposure of OPCs can affect the processes in immune response, carbohydrate metabolism, cardiovascular toxicity, and several others. Studies revealed that the toxicity of OPCs was provoked by inhibition of acetylcholinesterase (AChE). Therefore, combined in silico approaches - pharmacophore-based 3D-QSAR model; docking and Molecular Dynamics (MD) - were used to assess the precise and comprehensive effects of series of known OP-derived compounds together with its -log LD50 values. The selected five-featured pharmacophore model - AAHHR.61 - displayed the highest correlation (R2 = .9166), cross-validated coefficient (Q2 = .8221), F = 63.2, Pearson-R = .9615 with low RMSE = .2621 values obtained using five component PLS factors. Subsequently, the well-validated model was then used as a 3D query to search novel OPCs using a high-throughput virtual screening technique. Simultaneously, the docking studies predicted the binding pose of the most active OPC in the MdAChE binding pocket. Additionally, the stability of docking was verified using MD simulation. The results revealed that OP22 and predicted lead compounds bound tightly to S315 of MdAChE through potential hydrogen bond interaction over time. Overall, this study might provide valuable insight into binding mode of OPCs and hit compounds to inhibit AChE in housefly.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, ß-secretase, monoamine oxidase B, glycogen synthase kinase 3ß, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity.

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q2LOO, Q2(F2), and Q2(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

Deciphering the crucial molecular properties of a series of Benzothiazole Hydrazone inhibitors that targets anti-apoptotic Bcl-xL protein.

The Bcl-2 family proteins are the central regulators of apoptosis. Due to its predominant role in cancer progression, the Bcl-2 family proteins act as attractive therapeutic targets. Recently, molecular series of Benzothiazole Hydrazone (BH) inhibitors that exhibits drug-likeness characteristics, which selectively targets Bcl-xL have been reported. In the present study, docking was used to explore the plausible binding mode of the highly active BH inhibitor with Bcl-xL; and Molecular Dynamics (MD) simulation was applied to investigate the stability of predicted conformation over time. Furthermore, the molecular properties of the series of BH inhibitors were extensively investigated by pharmacophore based 3D-QSAR model. The docking correctly predicted the binding mode of the inhibitor inside the Bcl-xL hydrophobic groove, whereas the MD-based free energy calculation exhibited the binding strength of the complex over the time period. Furthermore, the residue decomposition analysis revealed the major energy contributing residues - F105, L108, L130, N136, and R139 - involved in complex stability. Additionally, a six-featured pharmacophore model - AAADHR.89 - was developed using the series of BH inhibitors that exhibited high survival score. The statistically significant 3D-QSAR model exhibited high correlation co-efficient (R2 = .9666) and cross validation co-efficient (Q2 = .9015) values obtained from PLS regression analysis. The results obtained from the current investigation might provide valuable insights for rational drug design of Bcl-xL inhibitor synthesis.

In silico strategies on prion pathogenic conversion and inhibition from PrPC -PrPSc.

INTRODUCTION: To date, various therapeutic strategies identified numerous anti-prion compounds and antibodies that stabilize PrPC, block the conversion of PrPC-PrPSc and increased effect on PrPSc clearance. However, no suitable drug has been identified clinically so far due to the poor oral absorption, low blood-brain-barrier [BBB] penetration, and high toxicity. Although some of the drugs were proven to be effective in prion-infected cell culture and whole animal models, none of them increased the rate of survival compared to placebo. Areas covered: In this review, the authors highlight the importance of in silico approaches like molecular docking, virtual screening, pharmacophore analysis, molecular dynamics, QSAR, CoMFA and CoMSIA applied to detect molecular mechanisms of prion inhibition and conversion from PrPC-PrPSc. Expert opinion: Several in silico approaches combined with experimental studies have provided many structural and functional clues on the stability and physiological activity of prion mutants. Further, various studies of in silico and in vivo approaches were also shown to identify several new small organic anti-scrapie compounds to decrease the accumulation of PrPres in cell culture, inhibit the aggregation of a PrPC peptide, and possess pharmacokinetic characteristics that confirm the drug-likeness of these compounds.