Drug-like index: a new approach to measure drug-like compounds and their diversity

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.     

Toward a catalytic cycle for the Mn-salen mediated alkene epoxidation: a computational approach

BP86 density functional calculations for the title reaction are presented, where a model catalyst with hypochlorite as oxygen-containing counter ligand, (ClO)(O)Mn(acacen') (acacen' = -O(CH)3N-C2H4-N(CH)3O-), is employed. The epoxidation reaction on potential energy surfaces corresponding to an overall spin-density of two and four unpaired electrons is investigated. The presence of the hypochlorite ligand is found to cause the reaction to proceed under conservation of spin. Further, the oxygen-containing counter ligand causes reoxidation of the Mn-center, thus closing the catalytic cycle. A catalytic scheme is therefore proposed, which includes a step of regeneration of the catalytically active species. Energetic estimates including corrections for solvent effects are presented for the relevant steps constituting the proposed catalytic scheme.     

Library fingerprints: a novel approach to the screening of virtual libraries

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a na?ve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.     

A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases

The discovery of various protein/receptor targets from genomic research is expanding rapidly. Along with the automation of organic synthesis and biochemical screening, this is bringing a major change in the whole field of drug discovery research. In the traditional drug discovery process, the industry tests compounds in the thousands. With automated synthesis, the number of compounds to be tested could be in the millions. This two-dimensional expansion will lead to a major demand for resources, unless the chemical libraries are made wisely. The objective of this work is to provide both quantitative and qualitative characterization of known drugs which will help to generate "drug-like" libraries. In this work we analyzed the Comprehensive Medicinal Chemistry (CMC) database and seven different subsets belonging to different classes of drug molecules. These include some central nervous system active drugs and cardiovascular, cancer, inflammation, and infection disease states. A quantitative characterization based on computed physicochemical property profiles such as log P, molar refractivity, molecular weight, and number of atoms as well as a qualitative characterization based on the occurrence of functional groups and important substructures are developed here. For the CMC database, the qualifying range (covering more than 80% of the compounds) of the calculated log P is between -0.4 and 5.6, with an average value of 2.52. For molecular weight, the qualifying range is between 160 and 480, with an average value of 357. For molar refractivity, the qualifying range is between 40 and 130, with an average value of 97. For the total number of atoms, the qualifying range is between 20 and 70, with an average value of 48. Benzene is by far the most abundant substructure in this drug database, slightly more abundant than all the heterocyclic rings combined. Nonaromatic heterocyclic rings are twice as abundant as the aromatic heterocycles. Tertiary aliphatic amines, alcoholic OH and carboxamides are the most abundant functional groups in the drug database. The effective range of physicochemical properties presented here can be used in the design of drug-like combinatorial libraries as well as in developing a more efficient corporate medicinal chemistry library.     

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour-Liquid equilibria or Liquid-Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid-Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔC_p term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔC_p term can be neglected and will not have a great impact on the results.     

CrystalDock: a novel approach to fragment-based drug design

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.     

Screening drug-like compounds by docking to homology models: a systematic study

In the absence of an experimentally solved structure, a homology model of a protein target can be used instead for virtual screening of drug candidates by docking and scoring. This approach poses a number of questions regarding the choice of the template to use in constructing the model, the accuracy of the screening results, and the importance of allowing for protein flexibility. The present study addresses such questions with compound screening calculations for multiple homology models of five drug targets. A central result is that docking to homology models frequently yields enrichments of known ligands as good as that obtained by docking to a crystal structure of the actual target protein. Interestingly, however, standard measures of the similarity of the template used to build the homology model to the targeted protein show little correlation with the effectiveness of the screening calculations, and docking to the template itself often is as successful as docking to the corresponding homology model. Treating key side chains as mobile produces a modest improvement in the results. The reasons for these sometimes unexpected results, and their implications for future methodologic development, are discussed.     

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (ΔG _sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because ΔG _sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient (R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for ΔG _sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental ΔG _sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental ΔG _sol data for organic solvents.     

Epitopes based drug design for dengue virus envelope protein: A computational approach

Dengue virus (DENV) has emerged as a rapidly spreading epidemic throughout the tropical and subtropical regions around the globe. No suitable drug has been designed yet to fight against DENV, therefore, the need for safe and effective antiviral drug has become imperative. The envelope protein of DENV is responsible for mediating the fusion process between viral and host membranes. This work reports an in silico approach to target B and T cell epitopes for dengue envelope protein inhibition. A conserved region “QHGTI” in B and T cell epitopes of dengue envelope glycoprotein was confirmed to be valid for targeting by visualizing its interactions with the host cell membrane TIM-1 protein which acts as a receptor for serotype 2 and 3. A reverse pharmacophore mapping approach was used to generate a seven featured pharmacophore model on the basis of predicted epitope. This pharmacophore model as a 3D query was used to virtually screen a chemical compounds dataset “Chembridge”. A total of 1010 compounds mapped on the developed pharmacophore model. These retrieved hits were subjected to filtering via Lipinski’s rule of five, as a result 442 molecules were shortlisted for further assessment using molecular docking. Finally, 14 hits of different structural properties having interactions with the active site residues of dengue envelope glycoprotein were selected as lead candidates. These structurally diverse lead candidates have strong likelihood to act as further starting structures in the development of novel and potential drugs for the treatment of dengue fever.     

Toward uniform nanotubular compounds: synthetic approach and ab initio calculations

We propose to synthesize a new class of single-walled nanotubular compounds (SWNCs) and investigate the interplay between their structural and electronic properties using ab initio density functional calculations. SWNCs are composed of cyclacenes of variable diameter interconnected by various linker compounds. Cyclacenes map directly onto and can be viewed as the shortest segments of (n,0) zigzag carbon nanotubes. We focus on cyclacenes with n=6-12 fused benzene rings interconnected by biphenyl, tetrazine, or acetylene linkers. Depending upon the nature and the orientation of the linkers, we find it possible to change the systems from narrow-gap to wide-gap semiconductors, and to modulate the band dispersion, suggesting the possibility of band gap engineering.