MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks

[Abstract] Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) in living beings or Atmospheric reaction networks in planets and moons. Network of neurons appear in the worm C. elegans, in Human brain connectome, or in Artificial Neural Networks (ANNs). Infection spreading networks exist for contagious outbreaks networks in humans and in malware epidemiology for infection with viral software in internet or wireless networks. Social-legal networks with different rules evolved from swarm intelligence, to hunter-gathered societies, or citation networks of U.S. Supreme Court. In all these cases, we can see the same question. Can we predict the links based on structural information? We propose to solve the problem using Quantitative Structure-Property Relationship (QSPR) techniques commonly used in chemo-informatics. In so doing, we need software able to transform all types of networks/graphs like drug structure, drug-target interactions, protein structure, protein interactions, metabolic reactions, brain connectome, or social networks into numerical parameters. Consequently, we need to process in alignment-free mode multitarget, multiscale, and multiplexing, information. Later, we have to seek the QSPR model with Machine Learning techniques. MI-NODES is this type of software. Here we review the evolution of the software from chemoinformatics to bioinformatics and systems biology. This is an effort to develop a universal tool to study structure-property relationships in complex systems

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates

In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular OPEN ACCESS Int. J. Mol. Sci. 2014, 15 17036 targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentallyThe authors thank the Xunta de Galicia for financial support of this work under project 07CSA008203PRS

Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p

Computational Analysis of Structure-Activity Relationships : From Prediction to Visualization Methods

Understanding how structural modifications affect the biological activity of small molecules is one of the central themes in medicinal chemistry. By no means is structure-activity relationship (SAR) analysis a priori dependent on computational methods. However, as molecular data sets grow in size, we quickly approach our limits to access and compare structures and associated biological properties so that computational data processing and analysis often become essential. Here, different types of approaches of varying complexity for the analysis of SAR information are presented, which can be applied in the context of screening and chemical optimization projects. The first part of this thesis is dedicated to machine-learning strategies that aim at de novo ligand prediction and the preferential detection of potent hits in virtual screening. High emphasis is put on benchmarking of different strategies and a thorough evaluation of their utility in practical applications. However, an often claimed disadvantage of these prediction methods is their "black box" character because they do not necessarily reveal which structural features are associated with biological activity. Therefore, these methods are complemented by more descriptive SAR analysis approaches showing a higher degree of interpretability. Concepts from information theory are adapted to identify activity-relevant structure-derived descriptors. Furthermore, compound data mining methods exploring prespecified properties of available bioactive compounds on a large scale are designed to systematically relate molecular transformations to activity changes. Finally, these approaches are complemented by graphical methods that primarily help to access and visualize SAR data in congeneric series of compounds and allow the formulation of intuitive SAR rules applicable to the design of new compounds. The compendium of SAR analysis tools introduced in this thesis investigates SARs from different perspectives

Development and application of fast fuzzy pharmacophore-based virtual screening methods for scaffold hopping

The goal of this thesis was the development, evaluation and application of novel virtual screening approaches for the rational compilation of high quality pharmacological screening libraries. The criteria for a high quality were a high probability of the selected molecules to be active compared to randomly selected molecules and diversity in the retrieved chemotypes of the selected molecules to be prepared for the attrition of single lead structures. For the latter criterion the virtual screening approach had to perform “scaffold hopping”. The first molecular descriptor that was explicitly reported for that purpose was the topological pharmacophore CATS descriptor, representing a correlation vector (CV) of all pharmacophore points in a molecule. The representation is alignment-free and thus renders fast screening of large databases feasible. In a first series of experiments the CATS descriptor was conceptually extended to the three-dimensional pharmacophore-pair CATS3D descriptor and the molecular surface based SURFCATS descriptor. The scaling of the CATS3D descriptor, the combination of CATS3D with different similarity metrics and the dependence of the CATS3D descriptor on the threedimensional conformations of the molecules in the virtual screening database were evaluated in retrospective screening experiments. The “scaffold hopping” capabilities of CATS3D and SURFCATS were compared to CATS and the substructure fingerprint MACCS keys. Prospective virtual screening with CATS3D similarity searching was applied for the TAR RNA and the metabotropic glutamate receptor 5 (mGlur5). A combination of supervised and unsupervised neural networks trained on CATS3D descriptors was applied prospectively to compile a focused but still diverse library of mGluR5 modulators. In a second series of experiments the SQUID fuzzy pharmacophore model method was developed, that was aimed to provide a more general query for virtual screening than the CATS family descriptors. A prospective application of the fuzzy pharmacophore models was performed for TAR RNA ligands. In a last experiment a structure-/ligand-based pharmacophore model was developed for taspase1 based on a homology model of the enzyme. This model was applied prospectively for the screening for the first inhibitors of taspase1. The effect of different similarity metrics (Euc: Euclidean distance, Manh: Manhattan distance and Tani: Tanimoto similarity) and different scaling methods (unscaled, scaling1: scaling by the number of atoms, and scaling2: scaling by the added incidences of potential pharmacophore points of atom pairs) on CATS3D similarity searching was evaluated in retrospective virtual screening experiments. 12 target classes of the COBRA database of annotated ligands from recent scientific literature were used for that purpose. Scaling2, a new development for the CATS3D descriptor, was shown to perform best on average in combination with all three similarity metrics (enrichment factor ef (1%): Manh = 11.8 ± 4.3, Euc = 11.9 ± 4.6, Tani = 12.8 ± 5.1). The Tanimoto coefficient was found to perform best with the new scaling method. Using the other scaling methods the Manhattan distance performed best (ef (1%): unscaled: Manh = 9.6 ± 4.0, Euc = 8.1 ± 3.5, Tani = 8.3 ± 3.8; scaling1: Manh = 10.3 ± 4.1, Euc = 8.8 ± 3.6, Tani = 9.1 ± 3.8). Since CATS3D is independent of an alignment, the dependence of a “receptor relevant” conformation might also be weaker compared to other methods like docking. Using such methods might be a possibility to overcome problems like protein flexibility or the computational expensive calculation of many conformers. To test this hypothesis, co-crystal structures of 11 target classes served as queries for virtual screening of the COBRA database. Different numbers of conformations were calculated for the COBRA database. Using only a single conformation already resulted in a significant enrichment of isofunctional molecules on average (ef (1%) = 6.0 ± 6.5). This observation was also made for ligand classes with many rotatable bonds (e.g. HIV-protease: 19.3 ± 6.2 rotatable bonds in COBRA, ef (1%) = 12.2 ± 11.8). On average only an improvement from using the maximum number of conformations (on average 37 conformations / molecule) to using single conformations of 1.1 fold was found. It was found that using more conformations actives and inactives equally became more similar to the reference compounds according to the CATS3D representations. Applying the same parameters as before to calculate conformations for the crystal structure ligands resulted in an average Cartesian RMSD of the single conformations to the crystal structure conformations of 1.7 ± 0.7 Å. For the maximum number of conformations, the RMSD decreased to 1.0 ± 0.5 Å (1.8 fold improvement on average). To assess the virtual screening performance and the scaffold hopping potential of CATS3D and SURFACATS, these descriptors were compared to CATS and the MACCS keys, a fingerprint based on exact chemical substructures. Retrospective screening of ten classes of the COBRA database was performed. According to the average enrichment factors the MACCS keys performed best (ef (1%): MACCS = 17.4 ± 6.4, CATS = 14.6 ± 5.4, CATS3D = 13.9 ± 4.9, SURFCATS = 12.2 ± 5.5). The classes, where MACCS performed best, consisted of a lower average fraction of different scaffolds relative to the number of molecules (0.44 ± 0.13), than the classes, where CATS performed best (0.65 ± 0.13). CATS3D was the best performing method for only a single target class with an intermediate fraction of scaffolds (0.55). SURFCATS was not found to perform best for a single class. These results indicate that CATS and the CATS3D descriptors might be better suited to find novel scaffolds than the MACCS keys. All methods were also shown to complement each other by retrieving scaffolds that were not found by the other methods. A prospective evaluation of CATS3D similarity searching was done for metabotropic glutamate receptor 5 (mGluR5) allosteric modulators. Seven known antagonists of mGluR5 with sub-micromolar IC50 were used as reference ligands for virtual screening of the 20,000 most drug-like compounds – as predicted by an artificial neural network approach – of the Asinex vendor database (194,563 compounds). Eight of 29 virtual screening hits were found with a Ki below 50 µM in a binding assay. Most of the ligands were only moderately specific for mGluR5 (maximum of > 4.2 fold selectivity) relative to mGluR1, the most similar receptor to mGluR5. One ligand exhibited even a better Ki for mGluR1 than for mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). All hits had different scaffolds than the reference molecules. It was demonstrated that the compiled library contained molecules that were different from the reference structures – as estimated by MACCS substructure fingerprints – but were still considered isofunctional by both CATS and CATS3D pharmacophore approaches. Artificial neural networks (ANN) provide an alternative to similarity searching in virtual screening, with the advantage that they incorporate knowledge from a learning procedure. A combination of artificial neural networks for the compilation of a focused but still structurally diverse screening library was employed prospectively for mGluR5. Ensembles of neural networks were trained on CATS3D representations of the training data for the prediction of “mGluR5-likeness” and for “mGluR5/mGluR1 selectivity”, the most similar receptor to mGluR5, yielding Matthews cc between 0.88 and 0.92 as well as 0.88 and 0.91 respectively. The best 8,403 hits (the focused library: the intersection of the best hits from both prediction tasks) from virtually ranking the Enamine vendor database (ca. 1,000,000 molecules), were further analyzed by two self-organizing maps (SOMs), trained on CATS3D descriptors and on MACCS substructure fingerprints. A diverse and representative subset of the hits was obtained by selecting the most similar molecules to each SOM neuron. Binding studies of the selected compounds (16 molecules from each map) gave that three of the molecules from the CATS3D SOM and two of the molecules from the MACCS SOM showed mGluR5 binding. The best hit with a Ki of 21 µM was found in the CATS3D SOM. The selectivity of the compounds for mGluR5 over mGluR1 was low. Since the binding pockets in the two receptors are similar the general CATS3D representation might not have been appropriate for the prediction of selectivity. In both SOMs new active molecules were found in neurons that did not contain molecules from the training set, i. e. the approach was able to enter new areas of chemical space with respect to mGluR5. The combination of supervised and unsupervised neural networks and CATS3D seemed to be suited for the retrieval of dissimilar molecules with the same class of biological activity, rather than for the optimization of molecules with respect to activity or selectivity. A new virtual screening approach was developed with the SQUID (Sophisticated Quantification of Interaction Distributions) fuzzy pharmacophore method. In SQUID pairs of Gaussian probability densities are used for the construction of a CV descriptor. The Gaussians represent clusters of atoms comprising the same pharmacophoric feature within an alignment of several active reference molecules. The fuzzy representation of the molecules should enhance the performance in scaffold hopping. Pharmacophore models with different degrees of fuzziness (resolution) can be defined which might be an appropriate means to compensate for ligand and receptor flexibility. For virtual screening the 3D distribution of Gaussian densities is transformed into a two-point correlation vector representation which describes the probability density for the presence of atom-pairs, comprising defined pharmacophoric features. The fuzzy pharmacophore CV was used to rank CATS3D representations of molecules. The approach was validated by retrospective screening for cyclooxygenase 2 (COX-2) and thrombin ligands. A variety of models with different degrees of fuzziness were calculated and tested for both classes of molecules. Best performance was obtained with pharmacophore models reflecting an intermediate degree of fuzziness. Appropriately weighted fuzzy pharmacophore models performed better in retrospective screening than CATS3D similarity searching using single query molecules, for both COX-2 and thrombin (ef (1%): COX-2: SQUID = 39.2., best CATS3D result = 26.6; Thrombin: SQUID = 18.0, best CATS3D result = 16.7). The new pharmacophore method was shown to complement MOE pharmacophore models. SQUID fuzzy pharmacophore and CATS3D virtual screening were applied prospectively to retrieve novel scaffolds of RNA binding molecules, inhibiting the Tat-TAR interaction. A pharmacophore model was built up from one ligand (acetylpromazine, IC50 = 500 µM) and a fragment of another known ligand (CGP40336A), which was assumed to bind with a comparable binding mode as acetylpromazine. The fragment was flexible aligned to the TAR bound NMR conformation of acetylpromazine. Using an optimized SQUID pharmacophore model the 20,000 most druglike molecules from the SPECS database (229,658 compounds) were screened for Tat-TAR ligands. Both reference inhibitors were also applied for CATS3D similarity searching. A set of 19 molecules from the SQUID and CATS3D results was selected for experimental testing. In a fluorescence resonance energy transfer (FRET) assay the best SQUID hit showed an IC50 value of 46 µM, which represents an approximately tenfold improvement over the reference acetylpromazine. The best hit from CATS3D similarity searching showed an IC50 comparable to acetylpromazine (IC50 = 500 µM). Both hits contained different molecular scaffolds than the reference molecules. Structure-based pharmacophores provide an alternative to ligand-based approaches, with the advantage that no ligands have to be known in advance and no topological bias is introduced. The latter is e.g. favorable for hopping from peptide-like substrates to drug-like molecules. A homology model of the threonine aspartase taspase1 was calculated based on the crystal structures of a homologous isoaspartyl peptidase. Docking studies of the substrate with GOLD identified a binding mode where the cleaved bond was situated directly above the reactive N-terminal threonine. The predicted enzyme-substrate complex was used to derive a pharmacophore model for virtual screening for novel taspase1 inhibitors. 85 molecules were identified from virtual screening with the pharmacophore model as potential taspase1- inhibitors, however biochemical data was not available before the end of this thesis. In summary this thesis demonstrated the successful development, improvement and application of pharmacophore-based virtual screening methods for the compilation of molecule-libraries for early phase drug development. The highest potential of such methods seemed to be in scaffold hopping, the non-trivial task of finding different molecules with the same biological activity.Ziel dieser Arbeit war die Entwicklung, Untersuchung und Anwendung von neuen virtuellen Screening-Verfahren für den rationalen Entwurf hoch-qualitativer Molekül-Datenbanken für das pharmakologische Screening. Anforderung für eine hohe Qualität waren eine hohe a priori Wahrscheinlichkeit für das Vorhandensein aktiver Moleküle im Vergleich zu zufällig zusammengestellten Bibliotheken, sowie das Vorhandensein einer Vielfalt unterschiedlicher Grundstrukturen unter den selektierten Molekülen, um gegen den Ausfall einzelner Leitstrukturen in der weiteren Entwicklung abgesichert zu sein. Notwendig für die letztere Eigenschaft ist die Fähigkeit eines Verfahrens zum „Grundgerüst-Springen“. Der erste Molekül-Deskriptor, der explizit für das „Grundgerüst-Springen“ eingesetzt wurde war der CATS Deskriptor – ein topologischer Korrelations-Vektor („correlation vector“, CV) über alle Pharmakophor-Punkte eines Moleküls. Der Vergleich von Molekülen über den CATS Deskriptor geschieht ohne eine Überlagerung der Moleküle, was den effizienten Einsatz solcher Verfahren für sehr große Molekül-Datenbanken ermöglicht. In einer ersten Serie von Versuchen wurde der CATS Deskriptor erweitert zu dem dreidimensionalen CATS3D Deskriptor und dem auf der Molekül-Oberfläche basierten SURFCATS Deskriptor. In retrospektiven Studien wurde für diese Deskriptoren der Einfluss verschiedener Skalierungs-Methoden, die Kombination mit unterschiedlichen Ähnlichkeits- Metriken und die Auswirkung verschiedener dreidimensionaler Konformationen untersucht. Weiter wurden das Potential der entwickelten Deskriptoren CATS3D und SURFCATS im „Grundgerüst-Springen“ mit CATS und dem Substruktur-Fingerprint MACCS keys verglichen. Prospektive Anwendungen der CATS3D Ähnlichkeitssuche wurden für die TARRNA und den metabotropen Glutamat Rezeptor 5 (mGluR5) durchgeführt. Eine Kombination von überwachten und unüberwachten neuronalen Netzen wurde prospektiv für die Zusammenstellung einer fokussierten aber dennoch diversen Bibliothek von mGluR5 Modulatoren eingesetzt. In einer zweiten Reihe von Versuchen wurde der SQUID Fuzzy Pharmakophor Ansatz entwickelt, mit dem Ziel zu einer noch generelleren Molekül- Beschreibung als mit den Deskriptoren aus der CATS Familie zu gelangen. Eine prospektive Anwendung der „Fuzzy Pharmakophor“ Methode wurde für die TAR-RNA durchgeführt. In einem letzten Versuch wurde für Taspase1 ein Struktur-/Liganden-basiertes Pharmakophor- Modell auf der Grundlage eines Homologie-Modells des Enzyms entwickelt. Dieses wurde für das prospektive Screening nach Taspase1-Inhibitoren eingesetzt. Der Einfluss verschiedener Ähnlichkeits-Metriken (Euk: Euklidische Distanz, Manh: Manhattan Distanz, Tani: Tanimoto Ähnlichkeit) und verschiedener Skalierungs-Methoden (Ohne-Skalierung, Skalierung1: Skalierung aller Werte nach der Anzahl Atome, Skalierung2: Skalierung der Werte eines Paares von Pharmakophor-Punkten entsprechend der Summe aller Pharmakophor-Punkte mit denselben Pharmakophor-Typen) auf die Ähnlichkeits-Suche mit CATS3D wurde in retrospektiven virtuellen Screening Experimenten untersucht. Für diesen Zweck wurden 12 verschiedene Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank von annotierten Liganden aus der jüngeren wissenschaftlichen Literatur eingesetzt. Skalierung2, eine neue Entwicklung für CATS3D, zeigte im Durchschnitt die beste Performanz in Kombination mit allen drei Ähnlichkeits-Metriken (Anreicherungs-Faktor ef (1%): Manh = 11,8 ± 4,3; Euk = 11,9 ± 4,6; Tani = 12,8 ± 5,1). Die Kombination von Skalierung2 mit dem Tanimoto Ähnlichkeits-Koeffizienten lieferte die besten Ergebnisse. In Kombination mit den anderen Skalierungen brachte die Manhattan Distanz die besten Ergebnisse (ef (1%): Ohne-Skalierung: Manh = 9,6 ± 4,0; Euk = 8,1 ± 3,5; Tani = 8,3 ± 3,8; Skalierung1: Manh = 10,3 ± 4,1; Euk = 8,8 ± 3,6; Tani = 9,1 ± 3,8). Da die CATS3D Ähnlichkeits-Suche unabhängig von der Überlagerung einzelner Moleküle ist, könnte ebenfalls eine gewisse Unabhängigkeit von der vorhandenen 3D Konformation bestehen. Eine solche Unabhängigkeit wäre interessant um die zeitaufwendige Berechnung multipler Konformationen zu umgehen. Um diese Hypothese zu untersuchen wurden Co-Kristalle von Liganden aus 11 Klassen von Rezeptoren und Enzymen ausgewählt, um als Anfrage-Strukturen im virtuellen Screening in der COBRA Datenbank zu dienen. Verschiedene Versionen der COBRA Datenbank mit unterschiedlicher Anzahl Konformationen wurden berechnet. Bereits mit einer einzigen Konformation pro Molekül konnte im Mittel eine deutliche Anreicherung an aktiven Molekülen beobachte werden (ef (1%) = 6,0 ± 6,5). Diese Beobachtung beinhaltete auch Klassen von Molekülen mit vielen rotierbaren Bindungen. (z.B. HIV-Protease: 19,3 ± 6,2 rotierbare Bindungen in COBRA, ef (1%) = 12,2 ± 11,8). Im Mittel konnten dazu bei Verwendung der maximalen Anzahl Konformationen (durchschnittlich 37 Konformationen / Molekül) nur eine Verbesserung von 1.1 festgestellt werden. Nach der CATS3D Ähnlichkeit wurden die inaktiven Moleküle im gleichen Maß ähnlicher zu den Referenzen als die aktiven Moleküle. Zum Vergleich konnte durch Verwendung multipler statt einzelner Konformationen eine 1,8-fache Verbesserung des RMSD zu den Konformationen aus den Kristall-Struktur Konformationen erreicht werden (einzelne Konformationen: 1,7 ± 0,7 Å; max. Konformationen: 1,0 ± 0,5 Å). Um die Leistungsfähigkeit von CATS3D und SURFCATS im virtuellen Screening und im Grundgerüst-Springen zu beurteilen, wurden diese Deskriptoren mit CATS und den MACCS keys, einem Fingerprint basierend auf exakten chemischen Substrukturen, verglichen. Für die retrospektive Analyse wurden 10 Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank ausgewählt. Nach den mittleren Anreicherungs-Faktoren ergaben sich für MACCS die besten Resultate (ef (1%): MACCS = 17,4 ± 6,4; CATS = 14,6 ± 5,4; CATS3D = 13,9 ± 4,9; SURFCATS = 12,2 ± 5,5). Es zeigte sich, dass die Klassen, in denen MACCS die besten Ergebnisse erzielen konnte, einen geringen gemittelten Anteil von verschiedenen Grundgerüsten aufwiesen im Verhältnis zu der Anzahl an Molekülen (0,44 ± 0,13) als die Klassen, in denen CATS am besten war (0,65 ± 0,13). CATS3D war nur in einer Klasse mit einem mittleren Anteil von Grundgerüsten (0,55) die beste Methode. SURFCATS war für keine Klasse besser als alle anderen Methoden. Diese Ergebnisse deuten darauf hin, dass Methoden wie CATS und CATS3D besser geeignet sind, um neue Grundgerüste zu finden. Es konnte weiter gezeigt werden, dass sich die Methoden einander ergänzen, dass also mit jeder Methode Grundgerüste gefunden werden konnten, die mit keiner der anderen Methoden gefunden werden konnten. Eine prospektive Anwendung wurde für CATS3D in der Suche nach neuen allosterischen Modulatoren des metabotropen Glutamat Rezeptors 5 (mGluR5) durchgeführt. Sieben bekannte allosterische mGluR5 Antagonisten mit sub-mikromolaren IC50 Werten wurde als Referenzen eingesetzt. Das virtuelle Screening wurde auf den 20.000 von einem künstlichen neuronalen Netz als am wirkstoff-artigsten vorhergesagten Molekülen der Asinex Datenbank (194.563 Moleküle) durchgeführt. Acht der 29 gefundenen Hits aus dem virtuellen Screening zeigten Ki Werte unter 50 µM in einem Bindungs-Assay. Die Mehrheit der Liganden zeigte nur eine geringe Selektivität (Maximum > 4,2-fach) gegenüber mGluR1, dem ähnlichsten Rezeptor zu mGluR5. Einer der Liganden zeigte einen besseren Ki für mGluR1 als für mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). Alle gefundenen Moleküle zeigten verschiedene Grundgerüste als die Referenz Moleküle. Es konnte gezeigt werden, dass die zusammengestellte Bibliothek von den MACCS keys als unterschiedlich zu den Referenz Strukturen betrachtet wurden, von CATS und CATS3D aber noch als isofunktional betracht wurden. Künstliche neuronal Netze („artificial neural net“, ANN) bieten eine Alternative zur Ähnlichkeits-Suche im virtuellen Screening mit dem Vorteil, dass in einer Serie von Liganden enthaltenes implizites Wissen über eine Lernprozedur in ein Modell integrierte werden kann. Eine Kombination von ANNs für die Zusammenstellung einer fokussierten aber dennoch diversen Molekül-Bibliothek wurde prospektiv für die Suche nach mGluR5 Antagonisten eingesetzt. Gruppen von ANNs wurden auf den Basis von CATS3D Repräsentationen für die Vorhersage von „mGluR5-artigkeit“ und „mGluR5/mGluR1 Selektivität“ trainiert. Dabei ergaben sich Matthews cc zwischen 0,88 und 0,92 sowie zwischen 0,88 und 0,91. Die besten 8.403 Hits (die Schnittmenge der besten Hits aus beiden Vorhersagen) aus einem virtuellen Screening der Enamine Datenbank (ca. 1.000.000 Moleküle) ergab die fokussierte Bibliothek. Diese wurde weiter mit Selbstor

Herramientas informáticas y de inteligencia artificial para el meta-análisis en la frontera entre la bioinformática y las ciencias jurídicas

[Resumen] Los modelos computacionales, conocidos por su acrónimo en idioma Inglés como QSPR (Quantitative Structure-Property Relationships) pueden usarse para predecir propiedades de sistemas complejos. Estas predicciones representan una aplicación importante de las Tecnologías de la Información y la Comunicación (TICs). La mayor relevancia es debido a la reducción de costes de medición experimental en términos de tiempo, recursos humanos, recursos materiales, y/o el uso de animales de laboratorio en ciencias biomoleculares, técnicas, sociales y/o jurídicas. Las Redes Neuronales Artificiales (ANNs) son una de las herramientas informáticas más poderosas para buscar modelos QSPR. Para ello, las ANNs pueden usar como variables de entrada (input) parámetros numéricos que cuantifiquen información sobre la estructura del sistema. Los parámetros conocidos como Índices Topológicos (TIs) se encuentran entre los más versátiles. Los TIs se calculan en Teoría de Grafos a partir de la representación de cualquier sistema como una red de nodos interconectados; desde moléculas a redes biológicas, tecnológicas, y sociales. Esta tesis tiene como primer objetivo realizar una revisión y/o introducir nuevos TIs y software de cálculo de TIs útiles como inputs de ANNs para el desarrollo de modelos QSPR de redes bio-moleculares, biológicas, tecnológico-económicas y socio-jurídicas. En ellas, por una parte, los nodos representan biomoléculas, organismos, poblaciones, leyes tributarias o concausas de delitos. Por otra parte, en la interacción TICs-Ciencias Biomoleculares- Derecho se hace necesario un marco de seguridad jurídica que permita el adecuado desarrollo de las TICs y sus aplicaciones en Ciencias Biomoleculares. Por eso, el segundo objetivo de esta tesis es revisar el marco jurídico-legal de protección de los modelos QSAR/QSPR de sistemas moleculares. El presente trabajo de investigación pretende demostrar la utilidad de estos modelos para predecir características y propiedades de estos sistemas complejos.[Resumo] Os modelos de ordenador coñecidos pola súas iniciais en inglés QSPR (Quantitative Structure-Property Relationships) poden prever as propiedades de sistemas complexos e reducir os custos experimentais en termos de tempo, recursos humanos, materiais e/ou o uso de animais de laboratorio nas ciencias biomoleculares, técnicas, e sociais. As Redes Neurais Artificiais (ANNs) son unha das ferramentas máis poderosas para buscar modelos QSPR. Para iso, as ANNs poden facer uso, coma variables de entrada (input), dos parámetros numéricos da estrutura do sistema chamados Índices Topolóxicos (TIs). Os TI calcúlanse na teoría dos grafos a partir da representación do sistema coma unha rede de nós conectados, incluíndo tanto moléculas coma redes sociais e tecnolóxicas. Esta tese ten como obxectivo principal revisar e/ou desenvolver novos TIs, programas de cálculo de TIs, e/ou modelos QSPR facendo uso de ANNs para predicir redes bio-moleculares, biolóxicas, económicas, e sociais ou xurídicas onde os nós representan moléculas biolóxicas, organismos, poboacións, ou as leis fiscais ou as concausas dun delito. Ademais, a interacción das TIC con as ciencias biolóxicas e xurídicas necesita dun marco de seguridade xurídica que permita o bo desenvolvemento das TIC e as súas aplicacións en Ciencias Biomoleculares. Polo tanto, o segundo obxectivo desta tese é analizar o marco xurídico e legal de protección dos modelos QSPR. O presente traballo de investigación pretende demostrar a utilidade destes modelos para predicir características e propiedades destes sistemas complexos.[Abstract] QSPR (Quantitative Structure-Property Relationships) computer models can predict properties of complex systems reducing experimental costs in terms of time, human resources, material resources, and/or the use of laboratory animals in bio-molecular, technical, and/or social sciences. Artificial Neural Networks (ANNs) are one of the most powerful tools to search QSPR models. For this, the ANNs may use as input variables numerical parameters of the system structure called Topological Indices (TIs). The TIs are calculated in Graph Theory from a representation of any system as a network of interconnected nodes, including molecules or social and technological networks. The first aim of this thesis is to review and/or develop new TIs, TIs calculation software, and QSPR models using ANNs to predict bio-molecular, biological, commercial, social, and legal networks where nodes represent bio-molecules, organisms, populations, products, tax laws, or criminal causes. Moreover, the interaction of ICTs with Biomolecular and law Sciences needs a legal security framework that allows the proper development of ICTs and their applications in Biomolecular Sciences. Therefore, the second objective of this thesis is to review the legal framework and legal protection of QSPR techniques. The present work of investigation tries to demonstrate the usefulness of these models to predict characteristics and properties of these complex systems

Modelos multi-escala de inteligencia artificial para diseño quimio-informático y fármaco-epidemiológico de terapias anti-VIH en Condados de Estados Unidos

[Resumen]Los métodos que relacionan la estructura química con la actividad biológica se conocen como “relaciones cuantitativas estructura-actividad” (en adelante QSAR). Es fundamental entender y cuantificar la relación entre la estructura y la actividad biológica de los potenciales fármacos para realizar su estudio eficiente. Este tipo de estudio consiste en correlacionar, por medio de descriptores moleculares, distintas propiedades químicas o fisicoquímicas de las moléculas en cuestión con valores de actividad biológica. Actualmente, el desarrollo de medicamentos más seguros y efectivos en el tratamiento de enfermedades como el SIDA es un objetivo que requiere del esfuerzo de un elevado número de especialistas en diferentes campos de la Ciencia, y donde el azar ha tenido un gran protagonismo. Sin embargo, parece razonable pensar que nunca se obtendrán medicamentos eficaces y seguros con sólo acudir al azar. Para ser más eficientes en el desarrollo de nuevos fármacos, la investigación en el tratamiento de las enfermedades requiere poseer mecanismos predictivos de algunas actividades. Los modelos basados en “redes de neuronas artificiales” (en adelante RRNNAA) son un ejemplo de modelos teóricos de predicción, ampliamente utilizados en muchas áreas de la Ciencia, como medicina, química, bioquímica…, así como también en el desarrollo de medicamentos. En esto último, son muy útiles para la predicción de propiedades de los potenciales fármacos. Las RRNNAA se aproximan a la forma de operar que usa el cerebro humano, con habilidad para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados por lo que se conoce como la “maldición de la cuádruple I”, por ser datos: inciertos, inconsistentes, incompletos e imprecisos. Esta particularidad hace que sean difíciles de gestionar adecuadamente por las técnicas computacionales convencionales, haciendo precisa la utilización de técnicas de Inteligencia Artificial, como son las ya citadas RRNNAA. La mayor ventaja de estos modelos inteligentes de predicción es que permiten evitar costes innecesarios producidos por desarrollos de nuevos compuestos con potencialidad terapéutica que resultarán estériles.Por lo tanto, el objetivo principal de la tesis aquí presentada es el desarrollo, con técnicas de inteligencia artificial, de una metodología “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos y pre-clínicos con datos epidemiológicos, para llevar a cabo predicciones “fármaco-epidemiológicas”, teniendo en cuenta la imposibilidad práctica y legal de obtener datos experimentales, en la fase IV del proceso de desarrollo de nuevos compuestos[Resumo]Os métodos que relacionan a estrutura química coa actividade biolóxica son chamados “relacións cuantitativas estrutura – actividade” (en adiante QSAR). É esencial para entender e cuantificar a relación entre a estrutura e a actividade biolóxica dos potenciais fármacos para realizar o seu estudio eficiente. Este tipo de estudo consiste en correlacionar, a través de descritores moleculares, distintas propiedades químicas ou fisicoquímicas de las moleculas en cuestión, con valores de actividade biolóxica. Actualmente, o desenvolvemento de medicamentos máis seguros e efectivos no tratamento de enfermidades como o SIDA é un obxectivo que require do esforzo de un gran número de especialistas en diferentes campos da ciencia, e onde o azar tivo un gran protagonismo. Nembergantes, parece razoable pensar que nunca se obterían medicamentos eficaces e seguros con só acudir ao azar. Para ser máis eficaces no desenvolvemento de novos farmacos, a investigación para o tratamento de enfermidades require mecanismos preditivos de algunhas actividades. Os modelos baseados en redes neurais artificiais (en adiante RRNNAA) son un exemplo de modelos teóricos de predición amplamente utilizado en moitas áreas da ciencia, como medicina, química, bioquímica..., así como tamén no desenvolvemento de medicamentos. Nesto último, son moi útiles para a predición de propiedades dos potenciais medicamentos. As RRNNAA achegánse ao xeito de funcionar do cerebro humano, coa capacidade para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados polo que se coñece como a “maldición da cuadrúple I”, por ser dados: incertos, inconsistentes, incompletos e imprecisos. Esta particularidade fai que sexan díficiles de xestionar axeitadamente coas técnicas computacionais convencionais, facendo preciso o uso de técnicas de Intelixencia Artificial, como son as xa citadas RRNNAA. A maior vantaxe destes modelos preditivos intelixentes é que permiten evitar custos innecesarios producidos polos desenvolvementos de novos compostos con potencial terapéutico que resultaran esteriles. Polo tanto o obxectivo principal da tese aquí presentada é o desenvolvemento, con tecnicas de intelixencia artificial dunha metodoloxía “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos e pre-clínicos con datos epidemiolóxicos, para levar a cabo predicións fármaco-epidemiolóxicas, tendo en conta a imposibilidade práctica e legal de obter datos experimentais na fase IV do proceso de desenvolvemento de novos compostos.[Abstract]The methods relating chemical structure to biological activity are called “Quantitative Structure Activity Relationships” (QSAR). It is essential to understand and quantify the relationships between the structure and biological activity of potential drugs to develop an efficient study on them. This kind of study consists of the correlation of the molecular descriptors based on several chemical or physicochemical properties with biological activity. Currently, the development of safer and more effective drugs in the treatment of diseases such as AIDS is a goal that requires a joint effort of a large number of specialists from different fields of science, and where chance also has a major role. However, it seems reasonable that no effective and safe drugs will be obtained based on chance only. To be more efficient in developing new drugs, the research for the treatment of diseases requires predictive mechanisms of some biological activities. The models based on "Artificial Neural Networks" (ANNs) are an example of theoretical prediction models, widely used in many areas of science such as Medicine, Chemistry, Biochemistry, etc. as well as in Drug Development. In the latter, they are very useful for predicting properties of potential drugs. ANNs approach the modus operandi used by the human brain, being able to successfully manage data, information and natural knowledge, or from the real world, which are affected by the so-called "curse of the fourfold I", dealing with information which is uncertain, inconsistent, incomplete and inaccurate. This feature makes it difficult to properly manage by conventional computational techniques, making the use of Artificial Intelligence (AI) techniques necessary, such as the above-mentioned ANNs. The most important advantage of these intelligent prediction models is the fact that they avoid unnecessary production costs associated with the development of new compounds with therapeutic potential which proved to be inactive. Therefore, the main objective of the thesis is the development of a chemoinformatics multi-scale methodology using artificial intelligence techniques to quantitatively relate chemical and pre-clinical data with epidemiological data, with the aim of performing "drug - epidemiological" predictions, taking into account the practical and legal impossibility of obtaining experimental data in Phase IV of the development process of new compounds

Construcción QSAR de redes complejas de compuestos de interés en Química Farmacéutica, Microbiología y Parasitología

El diseño para la búsqueda y desarrollo de fármacos eficaces para el tratamiento de estas enfermedades, que supriman la eliminación o la degeneración celular respectivamente, es una de las líneas de investigación más importantes dentro de la química farmacéutica. En esto entra el diseño de fármacos; el diseño de fármacos está dedicado al desarrollo de modelos matemáticos para predecir propiedades de interés para una gran variedad de sistemas químicos incluyendo moléculas de bajo peso molecular, polímeros, biopolímeros, sistemas heterogéneos, formulaciones farmacéuticas, conglomerados de moléculas e iones, materiales, nano-estructuras y otros. Este tipo de predicciones no pretenden sustituir las técnicas experimentales sino complementar las mismas ayudando a obtener nuevas moléculas activas con mayor probabilidad de éxito, con la ventaja que ello supone en términos de ahorro de tiempo, recursos materiales, y muy importante: el refinamiento y reducción en el uso de animales de laboratorio. Esta metodología se basa en el uso de cálculos por ordenador y en las nuevas tecnologías de la informática. Las cuales pueden ser usadas: Para moléculas pequeñas: a) Estudios de relación cuantitativa estructura molecular-actividad farmacológica (QSAR) y de estructura molecular propiedades toxicológicas y eco-toxicológicas incluyendo mutagenicidad e carcinogénesis (QSTR). b) Predicción de propiedades químicas y fisicoquímicas de moléculas. Estudios de relación estructura molecular y propiedades de absorción, distribución, metabolismo y eliminación (ADME). c) Predicción de mecanismos de acción biológica de moléculas y evaluación in sílico de alta eficacia para grandes bases de datos (virtual HTS). Para macromoléculas: a) Estudios de interacción fármaco-receptor (neuronas). b) Bioinformática aplicada a estudios de relación secuencia-función y propiedades estructurales de ácidos nucleicos y proteínas. c) Búsqueda de nuevas dianas terapéuticas y “sitio activo” a partir de datos de Genómica, Proteómica. d) Búsqueda de biomarcadores para diagnóstico de enfermedades o como indicadores de contaminaciones. e) Predicción de propiedades fisicoquímicas de polímeros sintéticos, biopolímeros, materiales y nano-estructuras. f) Predicción, diseño, y optimización de enzimas mutadas para procesos biotecnológicos

Modelos bioinformáticos y estudio de receptores de proteínas mediante el uso de redes complejas para el desarrollo y diseño de fármacos eficaces en patologías del sistema nervioso central

La búsqueda y desarrollo de fármacos eficaces para el tratamiento de enfermedades neurodegenerativas ha generado grandes expectativas, debido a la relevancia que tienen sobre la economía de los sistemas sanitarios y la tremenda carga y desgaste que sufren familia y cuidadores. Por ello, la industria farmacéutica se ha volcado sobre estas patologías en las últimas tres décadas, pero las dificultades de realizar ensayos sobre el SN provoca que los gastos y tiempos de investigación se disparen, limitando de forma considerable la rentabilidad de los procesos tradicionales en el desarrollo de nuevos medicamentos. Es en este apartado donde realiza sus aportaciones el diseño de fármacos, dedicando una parte del mismo al desarrollo de modelos matemáticos que permitan predecir propiedades de interés para una gran variedad de sistemas químicos incluyendo moléculas de bajo peso molecular, polímeros, biopolímeros, sistemas heterogéneos, formulaciones farmacéuticas, conglomerados de moléculas e iones, materiales, nano-estructuras y otros. En dicho sentido, los estudios QSAR (Quantitative Structure-Activity-Relationships) son usados cada vez mas como herramientas para el descubrimiento molecular. Estos modelos QSAR pueden ser diseñados para que predigan la probabilidad de que un fármaco sea efectivo contra una enfermedad degenerativa determinada ya sea la enfermedad de Parkinson, Alzheimer o cualquier otra, actuando sobre una diana molecular específica. En esta memoria presentamos de manera conjunta la revisión de modelos previos y trabajos específicos novedosos, en los que se han introducido nuevos índices numéricos utilizados para describir tanto la estructura molecular de fármacos como la estructura macromolecular de sus dianas o receptores (proteínas y/o ADN/ARN). Con estos ITs hemos sido capaces de desarrollar nuevos modelos multiQSAR de gran interés por su doble función en la predicción de fármacos y sus dianas moleculares. Estos trabajos permitirán la introducción de nuevos conceptos teóricos y la evolución hacia modelos con posibles aplicaciones en la búsqueda de nuevos fármacos neuroprotectores útiles en el tratamiento de las enfermedades de Parkinson y Alzheimer y/o nuevas dianas moleculares para estos fármacos. Este tipo de investigación abarca un área general-básica en la que interactúan la Bioinformática y la Quimioinformática