Article: Disease-Specific Differentiation Between Drugs and Non-Drugs Using Principal Component Analysis of Their Molecular Descriptor Space[show abstract] [hide abstract]
ABSTRACT: The physicochemical descriptor space has been extensively mapped and described in the literature for orally administered drugs and lead compounds. However, consideration of negative examples (non-drugs) or disease pathophysiology is not common in many studies. In the present work, a principal component analysis was carried out using drugs and non-drugs taking into account disease- and organ-specific categories, as well as different administration routes in addition to oral. The study involves 1386 relevant small-molecules including natural and synthetic products. Drug-specific as well as disease-category-specific or organ-specific regions and their respective threshold sets (ranges of descriptors) relative to non-drugs were elucidated on the scores plot and validated with external, independent sets of drugs and non-drugs. The respective loadings plot of molecular descriptors was rationalized in terms of physicochemically relevant groups related to the components of solvation free energy. The results of this analysis can contribute to the improved profiling of drug candidates and libraries making use of disease- and organ-specificity coded by physicochemical descriptors and ligand binding efficiency.Molecular Informatics. 05/2012; 31:369-383.
Article: Combined approach using ligand efficiency, cross-docking, and antitarget hits for wild-type and drug-resistant Y181C HIV-1 reverse transcriptase.[show abstract] [hide abstract]
ABSTRACT: New hits against HIV-1 wild-type and Y181C drug-resistant reverse transcriptases were predicted taking into account the possibility of some of the known metabolism interactions. In silico hits against a set of antitargets (i.e., proteins or nucleic acids that are off-targets from the desired pharmaceutical target objective) are used to predict a simple, visual measure of possible interactions for the ligands, which helps to introduce early safety considerations into the design of compounds before lead optimization. This combined approach consists of consensus docking and scoring: cross-docking to a group of wild-type and drug-resistant mutant proteins, ligand efficiency (also called binding efficiency) indices as new ranking measures, pre- and postdocking filters, a set of antitargets and estimation, and minimization of atomic clashes. Diverse, small-molecule compounds with new chemistry (such as a triazine core with aromatic side chains) as well as known drugs for different applications (oxazepam, chlorthalidone) were highly ranked to the targets having binding interactions and functional group spatial arrangements similar to those of known inhibitors, while being moderate to low binders to the antitargets. The results are discussed on the basis of their relevance to medicinal and computational chemistry. Optimization of ligands to targets and off-targets or antitargets is foreseen to be critical for compounds directed at several simultaneous sites.Journal of Chemical Information and Modeling 08/2011; 51(10):2595-611. · 4.68 Impact Factor
Article: Free Energy Calculations of Mutations Involving a Tightly Bound Water Molecule and Ligand Substitutions in a Ligand-Protein ComplexAlfonso T. Garcia-Sosa, Ricardo L. ManceraMolecular Informatics. 09/2010; 29:589-600.
Article: Design of multi-binding-site inhibitors, ligand efficiency, and consensus screening of avian influenza H5N1 wild-type neuraminidase and of the oseltamivir-resistant H274Y variant.[show abstract] [hide abstract]
ABSTRACT: The binding sites of wild-type avian influenza A H5N1 neuraminidase, as well as those of the Tamiflu (oseltamivir)-resistant H274Y variant, were explored computationally to design inhibitors that target simultaneously several adjacent binding sites of the open conformation of the virus protein. The compounds with the best computed free energies of binding, in agreement by two docking methods, consensus scoring, and ligand efficiency values, suggest that mimicking a polysaccharide, beta-lactam, and other structures, including known drugs, could be routes for multibinding site inhibitor design. This new virtual screening method based on consensus scoring and ligand efficiency indices is introduced, which allows the combination of pharmacodynamic and pharmacokinetic properties into unique measures.Journal of Chemical Information and Modeling 11/2008; 48(10):2074-80. · 4.68 Impact Factor
Article: DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties.[show abstract] [hide abstract]
ABSTRACT: The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.Journal of Chemical Information and Modeling 07/2012; 52(8):2165-80. · 4.68 Impact Factor