Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Finding Lead Compounds for Dengue Antivirals from a Collection of Old Drugs through In Silico Target Prediction and Subsequent In Vitro Validation

Dengue virus (DENV) infection is one of the most widely spread flavivirus infections. Despite the fatality it could cause, no antiviral treatment is currently available to treat the disease. Hence, this study aimed to repurpose old drugs as novel DENV NS3 inhibitors. Ligand-based (L-B) and proteochemometric (PCM) prediction models were built using 62,354 bioactivity data to screen for potential NS3 inhibitors. Selected drugs were then subjected to the foci forming unit reduction assay (FFURA) and protease inhibition assay. Finally, molecular docking was performed to validate these results. The in silico studies revealed that both models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation in terms of its sensitivity (0.671). In the in vitro validation, all drugs (zileuton, trimethadione, and linalool) were able to moderately inhibit the viral activities at the highest concentration tested. Zileuton showed comparable results with linalool when tested at 2 mM against the DENV NS3 protease, with a reduction of protease activity at 17.89 and 18.42%, respectively. Two new compounds were also proposed through the combination of the selected drugs, which are ziltri (zilueton + trimethadione) and zilool (zileuton + linalool). The molecular docking study confirms the in vitro observations where all drugs and proposed compounds were able to achieve binding affinity ≥ -4.1 kcal/mol, with ziltri showing the highest affinity at -7.7 kcal/mol, surpassing the control, panduratin A. The occupation of both S1 and S2 subpockets of NS2B-NS3 may be essential and a reason for the lower binding energy shown by the proposed compounds compared to the screened drugs. Based on the results, this study provided five potential new lead compounds (ziltri, zilool, zileuton, linalool, and trimethadione) for DENV that could be modified further.

Descriptor-free QSAR: effectiveness in screening for putative inhibitors of FGFR1

The long short-term memory (LSTM) algorithm has provided solutions to the limitations of the descriptors-utilizing QSAR models in drug design. However, the direct application of LSTM remains scarce. The effectiveness of a descriptor-free QSAR (LSTM-SM) in modeling the FGFR1 inhibitors dataset while comparing with two conventional QSAR using descriptors (126 bits Morgan fingerprint and 2 D descriptors respectively) as a baseline model was investigated in this study. The validated descriptor-free QSAR model was thereafter used to screen for active FGFR1 inhibitors in the ChemDiv database and subjected to molecular docking, induced-fit docking, QM-MM optimization, and molecular dynamics simulations to filter for compounds with high binding affinity and suggest the putative mechanism of inhibition and specificity. The LSTM-SM model performed better than conventional QSAR; having accuracy, specificity, and sensitivity of 0.92, model loss of 0.025, and AUC of 0.95. Fifteen thousand compounds were predicted as actives from the ChemDiv database and four compounds were finally selected. Of the four, two showed putatively effective binding interactions with key active site residues. Molecular dynamics simulations on these compounds in complex with the receptor further give insight into the conformational dynamics of each compound bounded to the receptor. The complexes formed are stable and exhibit a similar degree of compactness. Our findings predicted the advent of self-feature extracting machine learning algorithms of compounds, and have provided the possibility of better predictive model quality that is not necessarily limited by compound descriptors. The putative FGFR1 inhibitors, with their mechanism of inhibition and specificity, were elucidated using this approachCommunicated by Ramaswamy H. Sarma.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus

Growing interest in environmental toxicity assessment using Thamnocephalus platyurus as organism has led to an increased availability of acute toxicity data. Despite this growing interest in tests with this organism, however, to the best of our knowledge there are no computational models to predict the acute toxicity in T. platyurus. In view of the limited number of in silico models for this crustacean, we developed Quantitative Structure-Activity Relationship (QSAR) models for the prediction of acute toxicity towards T. platyurus, reflected by the 24h LC_50, using publicly available data according to the ISO 14380:2011 guideline. Two models were developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). We used partial least squares and gradient boosting machine techniques, which gave encouraging statistical quality in our data set.

Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening

Histone deacetylases (HDACs) as an important family of epigenetic regulatory enzymes are implicated in the onset and progression of carcinomas. As a result, HDAC inhibition has been proven as a compelling strategy for reversing the aberrant epigenetic changes associated with cancer. However, non-selective profile of most developed HDAC inhibitors (HDACIs) leads to the occurrence of various side effects, limiting their clinical utility. This evidence provides a solid ground for ongoing research aimed at identifying isoform-selective inhibitors. Among the isoforms, HDAC1 have particularly gained increased attention as a preferred target for the design of selective HDACIs. Accordingly, in this paper, we have developed a reliable virtual screening process, combining different ligand- and structure-based methods, to identify novel benzamide-based analogs with potential HDAC1 inhibitory activity. For this purpose, a focused library of 736,160 compounds from PubChem database was first compiled based on 80% structural similarity with four known benzamide-based HDAC1 inhibitors, Mocetinostat, Entinostat, Tacedinaline, and Chidamide. Our inclusive in-house 3D-QSAR model, derived from pharmacophore-based alignment, was then employed as a 3D-query to discriminate hits with the highest predicted HDAC1 inhibitory activity. The selected hits were subjected to subsequent structure-based approaches (induced-fit docking (IFD), MM-GBSA calculations and molecular dynamics (MD) simulation) to retrieve potential compounds with the highest binding affinity for HDAC1 active site. Additionally, in silico ADMET properties and PAINS filtration were also considered for selecting an enriched set of the best drug-like molecules. Finally, six top-ranked hit molecules, CID_38265326, CID_56064109, CID_8136932, CID_55802151, CID_133901641 and CID_18150975 were identified to expose the best stability profiles and binding mode in the HDAC1 active site. The IFD and MD results cooperatively confirmed the interactions of the promising selected hits with critical residues within HDAC1 active site. In summary, the presented computational approach can provide a set of guidelines for the further development of improved benzamide-based derivatives targeting HDAC1 isoform.

Antimicrobial Isoflavones and Derivatives from Erythrina (Fabaceae): Structure Activity Perspective (Sar & Qsar) on Experimental and Mined Values Against Staphylococcus Aureus

Prenylated (iso)flavonoids, -flavans and pterocarpans from taxa in Erythrina are repeatedly flagged as potent antimicrobial compounds. In the current study, bark from E. lysistemon was extracted and seven isoflavone derivatives were purified: erybraedin A (1), phaseollidin (2), abyssinone V-4′ methyl ether (3), eryzerin C (4), alpumisoflavone (5), cristacarpin (6) and lysisteisoflavone (7). Minimum inhibition concentration (MIC) values were determined against a range of species of bacteria (skin pathogens), then values for another 67 derivatives from Erythrina, only against Staphylococcus aureus, were mined from the literature. Of the seven isolates, MIC values widely ranged from 1–600 μg/mL, with no obvious pattern of selectivity for Gram-types. Nevertheless, using the mined and experimentally determined values against S. aureus, Klekota-Roth fragments (Structure Activity Relationship: SAR) were determined then used as molecular descriptors to make a ‘decision tree’ based on structural characters inspired by the classes of antimicrobial potency (classes A-D). Furthermore, to make quantitative predictions of MIC values (Quantitative SAR: QSAR) ‘pace regression’ was utilized and validated (R² = 0.778, Q² = 0.727 and P² = 0.555). Evidently, the position and degree of prenylation is important; however, the presence of hydroxyl groups at positions 5 and 7 in ring A and 4′ in ring B is associated with lower MIC values. While antimicrobial results continue to validate the traditional use of E. lysistemon extracts (or Erythrina generally) in therapeutic applications consistent with anti-infection, it is surprising that this class of compound is not being utilized more often in general industry applications, such as food or cosmetic preservation, or in topical antimicrobial creams. Prenylated (iso)flavonoids are derived from several other Genera, such as Dorstenia (Moraceae), Ficus (Moraceae), Glycyrrhiza (Fabaceae), Paulownia (Lamiales) or Pomifera (Moraceae).

Free radical scavenging mechanism of 1,3,4-oxadiazole derivatives: thermodynamics of O-H and N-H bond cleavage

The thermodynamics of free radical scavenge of 1,3,4-oxadiazole derivatives towards oxygen-centred free radicals were investigated by the density functional theory (DFT) method in the gas phase and aqueous solution. Three mechanisms of free radical scavenge namely, hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET) were considered. The antioxidant descriptors that characterize these mechanisms such as, bond dissociation enthalpy (BDE), adiabatic ionization potential (AIP), proton dissociation enthalpy (PDE), proton affinity (PA) and electron transfer enthalpy (ETE) were evaluated. The sequence of electron donation as predicted by the HOMO results were in good agreement with the sequence of ETE for the considered molecules at their favoured sites of free radical scavenge. The reaction Gibbs free energy for inactivation of the selected peroxyl radicals, show that 1,3,4-oxadiazole antioxidants are more efficient radical scavengers by HAT and SPLET mechanisms than SET-PT mechanism in vacuum. In aqueous solution, the SET-PT mechanism was observed to be the dominant reaction pathway.

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2

A dataset of seventy-two (72) cytotoxic compounds of the National Cancer Institute (NCI) was studied by QSAR and docking approaches to gain deeper insights into ligands selectivity on SK-MEL-2 cell line. The QSAR model was built using fifty (50) molecules and the best-generated model based on multiple linear regression showed, respectively good quality of fits (R 2 (0.864),R a d j u s t e d 2 (0.845), Q2 cv (0.799) andR p r e d 2 (0.706)). The model's predictive ability was determined by a test set of twenty-two (22) compounds. Compounds 30 and 41 were selected as templates for in silico design because they had high pGI50 activity and are in the model's applicability domain. The obtained information from the model was explored to design novel molecules by introducing various modifications. Moreover, the designed compounds with better-predicted activity (pGI50) values were selected and docked on the active site of the protein (PDB-CODE: 3OG7) which is responsible for melanoma cancer to elucidate their binding mode. AN2 (-12.1kcalmol-1) and AC4 (-12.4kcalmol-1) showed a better binding score for the target when compared with (vemurafenib, -11.3kcalmol-1) the known inhibitor of the target (V600E-BRAF). These findings may be very helpful in early anti-cancer drug development.

In silico design of hydrazone antioxidants and analysis of their free radical-scavenging mechanism by thermodynamic studies

Background

Antioxidants are very crucial in maintaining the normal function of body cells, as they scavenge excess free radical in the body. A set of hydrazone antioxidants was designed by in silico screening. The density functional theory (DFT) method was employed to explore the reaction energetics of their free radical-scavenging mechanism. With the aid of the developed quantitative structure-activity relationship (QSAR) model for hydrazone antioxidants, the structure and antioxidant activity of these compounds were predicted. Three potential reaction mechanisms were investigated, namely, hydrogen atom transfer (HAT), single-electron transfer followed by proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET). Bond dissociation enthalpy (BDE), adiabatic ionization potential (AIP), proton dissociation enthalpy (PDE), proton affinity (PA), electron transfer enthalpy (ETE) and Gibbs free energy that characterize the various steps in these mechanisms were calculated in the gas phase.

Results

A total of 25 hydrazone antioxidants were designed, in which the molecule MHD 017 gave the best antioxidant activity. Among the tested molecules, MHD 017 at the 10-OH site gave the best results for the various thermodynamic parameters calculated. The reaction Gibbs free energy results also indicate that this is the most favoured site for free radical scavenge.

Conclusion

The obtained results show that HAT and SPLET mechanisms are the thermodynamically plausible reaction pathways of free radical scavenge by hydrazone antioxidants. The reactivity of these compounds towards the hydroperoxyl radical (HOO·) was greater than that towards the methyl peroxyl radical (CH3OO·) based on the exergonicity of the calculated reaction Gibbs free energy.

Graphical abstract

In Silico Identification of Potential Inhibitor Against a Fungal Histone Deacetylase, RPD3 from Magnaporthe Oryzae

Histone acetylation and deacetylation play an essential role in the epigenetic regulation of gene expression. Histone deacetylases (HDAC) are a group of zinc-binding metalloenzymes that catalyze the removal of acetyl moieties from lysine residues from histone tails. These enzymes are well known for their wide spread biological effects in eukaryotes. In rice blast fungus, Magnaporthe oryzae, MoRPD3 (an ortholog of Saccharomyces cerevisiae Rpd3) was shown to be required for growth and development. Thus in this study, the class I HDAC, MoRpd3 is considered as a potential drug target, and its 3D structure was modelled and validated. Based on the model, a total of 1880 compounds were virtually screened (molecular docking) against MoRpd3 and the activities of the compounds were assessed by docking scores. The in silico screening suggested that [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.7 kcal/mol) and [4-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.5 kcal/mol) are effective in comparison to trichostatin A (−7.9 kcal/mol), a well-known general HDAC inhibitor. The in vitro studies for inhibition of appressorium formation by [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid has resulted in the maximum inhibition at lower concentrations (1 μM), while the trichostatin A exhibited similar levels of inhibition at 1.5 μM. These findings thus suggest that 3D quantitative structure activity relationship studies on [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid compound can further guide the design of more potential and specific HDAC inhibitors.

Molecular docking and ADMET analysis of hydroxamic acids as HDAC2 inhibitors

Histone deacetylase (HDAC2) belongs to the hydrolase family and a promising target for cancers. We reported 96 hydroxamic compounds optimized using hydrogen-donors, hydrophobic and electron withdrawing groups followed by molecular docking studies. The optimized compounds show good LibDock score and H-bond interaction in the active site of HDAC2. We selected 20 compounds as the best HDAC2 inhibitors based on the LibDock score, binding energy and hydrogen bonding. ADMET predictions on these compounds show good absorption, BBB penetration and no liver toxicity. We subsequently report four compounds selected as best HDAC2 inhibitors based on the LibDock, binding energy, H-bonding and ADMET properties.

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

Purpose

This study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain.

Methods

We retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families.

Results

Internal validation of the selected models returned Q² values close to 0.60. External validation also gave r² values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r² 0.74 in external validation.

Conclusions

Performance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.

Virtual screening and experimental validation of novel histone deacetylase inhibitors

Background

Histone deacetylases (HDACs) are promising therapeutic targets for the treatment of cancer, diabetes and other human diseases. HDAC inhibitors, as a new class of potential therapeutic agents, have attracted a great deal of interest for both research and clinical applications. Increasing efforts have been focused on the discovery of HDAC inhibitors and some HDAC inhibitors have been approved for use in cancer therapy. However, most HDAC inhibitors, including the clinically approved agents, do not selectively inhibit the deacetylase activity of class I and II HDAC isforms, and many suffer from metabolic instability. This study aims to identify new HDAC inhibitors by using a high-throughput virtual screening approach.

Methods

An integration of in silico virtual screening and in vitro experimental validation was used to identify novel HDAC inhibitors from a chemical database.

Results

A virtual screening workflow for HDAC inhibitors were created by integrating ligand- and receptor- based virtual screening methods. Using the virtual screening workflow, 22 hit compounds were selected and further tested via in vitro assays. Enzyme inhibition assays showed that three of the 22 compounds had HDAC inhibitory properties. Among these three compounds, ZINC12555961 significantly inhibited HDAC activity. Further in vitro experiments indicated that ZINC12555961 can selectively inhibit proliferation and promote apoptosis of cancer cells.

Conclusions

In summary, our study presents three new and potent HDAC inhibitors and one of these HDAC inhibitors shows anti-proliferative and apoptosis-inducing activity against various cancer cell lines. These results suggest that the developed virtual screening workflow can provide a useful source of information for the screening and validation of new HDAC inhibitors. The new-found HDAC inhibitors are worthy to further and more comprehensive investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-016-0075-8) contains supplementary material, which is available to authorized users.

Screening of hepatitis C NS5B polymerase inhibitors containing benzothiadiazine core: a steered molecular dynamics

Hepatic C virus (HCV) is a global health problem, resulting in liver cirrhosis and inflammation that can develop to hepatocellular carcinoma and fatality. The NS5B polymerase of HCV plays an important role in viral RNA replication process, making it an attractive therapeutic target for design and development of anti-HCV drugs. To search new potent compounds against the HCV NS5B polymerase, the molecular docking and the steered molecular dynamics (SMD) simulation techniques were performed. The potential potent inhibitors of the NS5B polymerase were screened out from the ZINC database using structural similarity search and molecular docking technique. Five top-hit compounds (the ZINC compounds 49888724, 49054741, 49777239, 49793673, and 49780355) were then studied by the SMD simulations based on the hypothesis that a high rupture force relates to a high binding efficiency. The results demonstrated that the ZINC compound 49888724 had a greater maximum rupture force, reflecting a good binding strength and inhibitory potency than known inhibitors and the rest four ZINC compounds. Therefore, our finding indicated that the ZINC compound 49888724 is a potential candidate to be a novel NS5B inhibitor for further design. Besides, the van der Waals interaction could be considered as the main contribution for stabilizing the NS5B-ligand complex.

Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.

3D-QSAR studies of azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives as anti-AChE and anti-AD agents by the CoMFA method

In the present study, a series of novel azaoxoisoaporphine derivatives were reported and their inhibitory activities toward acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), and Aβ aggregation were evaluated. The new compounds remained high inhibitory potency on Aβ aggregation, with inhibitory activity from 29.42% to 89.63% at a concentration of 10μM, but had no action on AChE or BuChE, which was very different from our previously reported oxoaporphine and oxoisoaporphine derivatives. By 3D-QSAR studies, we constructed a reliable CoMFA model (q(2)=0.856 and r(2)=0.986) based on the inhibitory activities toward AChE and discovered key information on structure and anti-AChE activities among the azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives. The model was further confirmed by the test-set validation (q(2)=0.873, r(2)=0.937, and slope k=0.902) and Y-randomization examination. The statistically significant and physically meaningful 3D-QSAR/CoMFA model provided better insight into understanding the inhibitory behaviors of those chemicals, which may provide useful information for the rational molecular design of azaoxoisoaporphine derivatives anti-AChE and anti-AD agents.

QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea

Quantitative structure - activity relationships (QSARs) developed to evaluate percentage of inhibition of STa-stimulated (Escherichia coli) cGMP accumulation in T84 cells are calculated by the Monte Carlo method. This endpoint represents a measure of biological activity of a substance against diarrhea. Statistical quality of the developed models is quite good. The approach is tested using three random splits of data into the training and test sets. The statistical characteristics for three splits are the following: (1) n=20, r(2)=0.7208, q(2)=0.6583, s=16.9, F=46 (training set); n=11, r(2)=0.8986, s=14.6 (test set); (2) n=19, r(2)=0.6689, q(2)=0.5683, s=17.6, F=34 (training set); n=12, r(2)=0.8998, s=12.1 (test set); and (3) n=20, r(2)=0.7141, q(2)=0.6525, s=14.7, F=45 (training set); n=11, r(2)=0.8858, s=19.5 (test set). Based on the proposed here models hypothetical compounds which can be useful agents against diarrhea are suggested.

Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors

Due to their role in the metabolism of monoamine neurotransmitters, MAO-A and MAO-B present a significant pharmacological interest. For instance the inhibitors of human MAO-B are considered useful tools for the treatment of Parkinson Disease. Therefore, the rational design and synthesis of new MAOs inhibitors is considered of great importance for the development of new and more effective treatments of Parkinson Disease. In this work, Quantitative Structure Activity Relationships (QSAR) has been developed to predict the human MAO inhibitory activity and selectivity. The first step was the selection of a suitable dataset of heterocyclic compounds that include chromones, coumarins, chalcones, thiazolylhydrazones, etc. These compounds were previously synthesized in one of our laboratories, or elsewhere, and their activities measured by the same assays and for the same laboratory staff. Applying linear discriminant analysis to data derived from a variety of molecular representations and feature selection algorithms, reliable QSAR models were built which could be used to predict for test compounds the inhibitory activity and selectivity toward human MAO. This work also showed how several QSAR models can be combined to make better predictions. The final models exhibit significant statistics, interpretability, as well as displaying predictive power on an external validation set made up of chromone derivatives with unknown activity (that are being reported here for first time) synthesized by our group, and coumarins recently reported in the literature.

QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC₅₀ of CXCR2 receptors for which no experimental data is available.

Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: an unexpected good prediction based on a model that seems untrustworthy

To validate QSAR models an external test set is increasingly used. However the definition of the compounds for the test set is still debated. We studied, co-evolutions of correlations between optimal descriptors and carcinogenicity (pTD50) for the subtraining, calibration, and test set. Weak correlations for the sub-training set are sometimes accompanied by quite good correlations for the external test set. This can be explained in terms of the probability theory and can help define a suitable test set. The simplified molecular input line entry system (SMILES) was used to represent the molecular structure. Correlation weights for calculating the optimal descriptors are related to fragments of the SMILES. The statistical quality of the model is: n=170, r2=0.6638, q2=0.6554, s=0.828, F=331 (sub-training set); n=170, r2=0.6609, r2pred=0.6520, s=0.825, F=331 (calibration set); and n=61, r2=0.7796, r2pred=0.7658, Rm2=0.7448, s=0.563, F=221 (test set). The calculations were done with CORAL software (http://www.insilico.eu/coral/).

QSAR modelling toxicity toward rats of inorganic substances by means of CORAL

CORAL (‘CORrelation And Logic’) is freeware available on the Internet www.insilico.eu/coral The aim of this program is to establish a correlation between an endpoint and descriptors calculated with a simplified molecular input line entry system (SMILES). Three models calculated by CORAL for toxicity towards rat (-pLD50) of inorganic substances (three random splits) have shown that CORAL could be a good tool to model this endpoint. The average statistical characteristics for the CORAL models are the following: n=38, r2=0.8461, q2=0.8298, s=0.273, F=198 (subtraining set); n=37, r2=0.8144, s=0.322, F=154 (calibration set); and n=10, r2=0.8004, Rm (test)2 =0.7815, s=0.240, F=32 (validation set).

In silico exploration for identifying structure-activity relationship of MEK inhibition and oral bioavailability for isothiazole derivatives

In this study, quantitative structure-activity/property models are developed for modeling and predicting both MEK inhibitory activity and oral bioavailability of novel isothiazole-4-carboxamidines. The models developed are thoroughly discussed to identify the key components that influence the inhibitory activity and oral bioavailability of the selected compounds. These selected descriptors serve as a first guideline for the design of novel and potent MEK inhibitors with desired ADME properties.

Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks

In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines

In the work described here, we developed the first multi-target quantitative structure-activity relationship (QSAR) model able to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns. GSK-3β inhibitors are interesting candidates for developing anti-Alzheimer compounds. GSK-3β are also of interest as anti-parasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The MARCH-INSIDE technique was used to quickly calculate total and local polarizability, n-octanol/water partition coefficients, refractivity, van der Waals area and electronegativity values to 4,508 active/non-active compounds as well as the average values of these indexes for active compounds in 42 different biological assays. Both the individual molecular descriptors and the average values for each test were used as input for a linear discriminant analysis (LDA). We discovered a classification function which used in training series correctly classifies 873 out of 1,218 GSK-3 cases of inhibitors (97.4%) and 2,140 out of 2,163 cases of non-active compounds (86.1%) in the 42 different tests. In addition, the model correctly classifies 285 out of 406 GSK-3 inhibitors (96.3%) and 710 out of 721 cases of non-active compounds (85.4%) in external validation series. The result is important because, for the first time, we can use a single equation to predict the results of heterogeneous series of organic compounds in 42 different experimental tests instead of developing, validating, and using 42 different QSAR models. Lastly, a double ordinate Cartesian plot of cross-validated residuals (first ordinate), standard residuals (second ordinate), and leverages (abscissa) defined the domain of applicability of the model as a squared area within ± 2 band for residuals and a leverage threshold of h = 0.0044.