An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Structure-based model profiles affinity constant of drugs with hPEPT1 for rapid virtual screening of hPEPT1’s substrate

The human proton-coupled peptide transporter (hPEPT1) with broad substrates is an important route for improving the pharmacokinetic performance of drugs. Thus, it is essential to predict the affinity constant between drug molecule and hPEPT1 for rapid virtual screening of hPEPT1’s substrate during lead optimization, candidate selection and hPEPT1 prodrug design. Here, a structure-based in silico model for 114 compounds was constructed based on eight structural parameters. This model was built by the multiple linear regression method and satisfied all the prerequisites of the regression models. For the entire data set, the r² and adjusted r² values were 0.74 and 0.72, respectively. Then, this model was used to perform substrate/non-substrate classification. For 29 drugs from DrugBank database, all were correctly classified as substrates of hPEPT1. This model was also used to perform substrate/non-substrate classification for 18 drugs and their prodrugs; this QSAR model also can distinguish between the substrate and non-substrate. In conclusion, the QSAR model in this paper was validated by a large external data set, and all results indicated that the developed model was robust, stable, and can be used for rapid virtual screening of hPEPT1’s substrate in the early stage of drug discovery.     

Investigation of novel compounds via in silico approaches of EGFR inhibitors as anticancer agents

Before the experimental studies of a compound to be synthesized from in vitro to in vivo, it is possible to save both time and money with in silico approaches only with Computer Aided Drug Design (CADD) methods. In other words, compounds that can be new drug candidates can be suggested by drug design using computational drug discovery strategies. In this study, all molecules in the ChEMBL Database were virtual screened based on drugs with inhibitory properties on the Epidermal Growth Factor Receptor (EGFR), one of the receptor tyrosine kinases, which is effective in cancer cells. During this High-Throughput Screening (HTS), the number of compounds was minimized according to the parameters of the reference drugs, physicochemical properties such as logP, M.W., HBA, HDB, RosLip. As a result of in silico approaches and molecular docking analysis, ten compounds with the highest docking scores were determined and a model compound that could be a new drug candidate was proposed.     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

Development of QSAR models for microsomal stability: identification of good and bad structural features for rat,human and mouse microsomal stability

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A,a selective kappa opioid receptor agonist

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based “agonist-bound” hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK _i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Structure-guided fragment-based<Emphasis Type="Italic"> in silico</Emphasis> drug design of dengue protease inhibitors

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC₅₀ = 7.7 μM and 37.9 μM, respectively) and the related West Nile virus protease (IC₅₀ = 6.3 μM and 39.0 μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.     

Simultaneous screening of four epidermal growth factor receptor antagonists from Curcuma longa via cell membrane chromatography online coupled with HPLC–MS

The epidermal growth factor receptors (EGFRs) are significant targets for screening active compounds. In this work, an analytical method was established for rapid screening, separation, and identification of EGFRs antagonists from Curcuma longa. Human embryonic kidney 293 cells with a steadily high expression of EGFRs were used to prepare the cell membrane stationary phase in a cell membrane chromatography model for screening active compounds. Separation and identification of the retention chromatographic peaks was achieved by HPLC–MS. The active sites, docking extents and inhibitory effects of the active compounds were also demonstrated. The screening result found that ar‐turmerone, curcumin, demethoxycurcumin, and bisdemethoxycurcumin from Curcuma longa could be active components in a similar manner to gefitinib. Biological trials showed that all of four compounds can inhibit EGFRs protein secretion and cell growth in a dose‐dependent manner, and downregulate the phosphorylation of EGFRs. This analytical method demonstrated fast and effective characteristics for screening, separation and identification of the active compounds from a complex system and should be useful for drug discovery with natural medicinal herbs.     

Preliminary studies of berberine and its semi-synthetic derivatives as a promising class of multi-target anti-parkinson agents

Parkinson’s disease (PD) is a neurodegenerative disorder bearing motor and nonmotor symptoms. The treatment today is symptomatical rather than preventive or curative and this leaves the field open for the search of both novel molecular targets and drug candidates. Interference with α-synuclein fibrillation, monoamine oxidase (MAO) inhibition, modulation of adenosine receptors and the inhibition of specific phosphodiesterase (PDE) isoforms are some of the currently pursued strategies. We synthesised and studied some semi-synthetic berberine derivatives using a set of in silico tools. We evaluated their drug-likeness and tested the compounds against a set of target proteins involved in the onset or progression of PD, with a particular attention to MAO-B. Preliminary in vitro assay on MAO-B confirmed our in silico predictions.     

A pH‐dependent computational approach to the effect of mutations on protein stability

This article describes a novel software implementation for high‐throughput scanning mutagenesis with a focus on protein stability. The approach combines molecular mechanics calculations with calculations of protein ionization and a Gaussian‐chain model of electrostatic interactions in unfolded state. Comprehensive testing demonstrates a state‐of‐the‐art accuracy for predicted free energy differences on single, double, and triple mutations with a correlation coefficient R above 0.7, which takes about 1.5 min per mutation on a single CPU. Unlike most of existing in silico methods for fast mutagenesis, the stability changes are reported as a continuous function of solution pH for wide pH intervals. We also propose a novel in silico strategy for searching stabilized protein variants that is based on combinatorial scanning mutagenesis using representative amino acid types. Our in silico predictions are in excellent agreement with the hyper‐stabilized variants of mesophilic cold shock protein found using the Proside method of direct evolution. © 2016 Wiley Periodicals, Inc.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

Prediction of human cytochrome P450 2B6‐substrate interactions using hierarchical support vector regression approach

The human cytochrome P450 2B6 can metabolize a number of clinical drugs. Inhibition of CYP2B6 by coadministered multiple drugs may lead to drug–drug interactions and undesired drug toxicity. The aim of this investigation is to develop an in silico model to predict the interactions between P450 2B6 and novel inhibitors using a novel hierarchical support vector regression (HSVR) approach, which simultaneously takes into account the coverage of applicability domain (AD) and the level of predictivity. Thirty‐seven molecules were deliberately selected and rigorously scrutinized from the literature data, of which 26 and 11 molecules were treated as the training set and the test set to generate the models and to validate the generated models, respectively. The generated HSVR model gave rise to an r² value of 0.97 for observed versus predicted pK_m values for the training set, a q² value of 0.93 by the 10‐fold cross‐validation, and an r² value of 0.82 for the test set. Additionally, the predicted results show that the HSVR model outperformed the individual local models, the global model, and the consensus model. Thus, this HSVR model provides an accurate tool for the prediction of human cytochrome P450 2B6‐substrate interactions and can be utilized as a primary filter to eliminate the potential selective inhibitor of CYP2B6. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009     

DG-AMMOS: A New tool to generate 3D conformation of small molecules using Distance Geometry and Automated Molecular Mechanics Optimization for in silico Screening

Background  

Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats.     

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein–ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein–ligand interactions.     

Screening active anti‐breast cancer compounds from Cortex Magnolia officinalis by 2D LC–MS

Most of the anti‐breast cancer drugs are often limited owing to drug resistance and serious adverse reactions. Therefore, development of more targeted and low toxic drugs from traditional Chinese medicines for breast cancer are needed. At the same time, establishment of fast and effective drug screening methods are urgently required. We describe here a 2D LC method of MDA‐MB‐231 cell membrane chromatography combined with HPLC/MS for recognition, separation, and identification of target components from traditional Chinese medicine Cortex Magnolia officinalis. The MDA‐MB‐231 cells membrane was used to prepare the chromatographic stationary phase in the first dimension. The active compounds had a retention characteristic on the cell membrane chromatography model (10 × 2.0 mm, 5 μm). The retention fractions were enriched using an online C₁₈ column (10 × 1.0 mm, 5 μm) and were analyzed by the second dimension RP chromatography. Finally, the activity of the retention fractions was tested through in vitro experiments. Results showed that the retention fractions were honokiol and magnolol and the inhibition rate on MDA‐MB‐231 cell growth were 23 and 64 μM, respectively. These results support the conclusion that this coupled analytical technique could be an efficient method in drug discovery.