Quantum theory of QSAR

Es discuteix aquí la forma de desenvolupar un formalisme on les mesures de semblança quàntiques (QSM) es transformen en un producte natural, que sorgeix d'un marc de treball específic relacionat amb la teoria quàntica. Aquesta fita s'empra per establir una connexió fonamental entre la teoria quàntica i les QSAR, que s'estudien més endavant des del punt de vista de la química quàntica discreta. A fi d'assolir aquest objectiu es revisen en un primer pas diverses eines teòriques. D'aquesta manera la primera secció s'associa a la construcció del concepte de conjunt etiquetat. Més tard, la definició d'objecte quàntic (QO) s'aclareix emprant tant el rerefons de la teoria quàntica com els conceptes previs, que formen part del formalisme de conjunt etiquetat. Per definir un QO, es demostra que les funcions de densitat (DF) tenen un paper principal i es presenta una possible forma matemàtica simplificada amb propòsits computacionals. En el camí de preparar les eines per dilucidar el problema, els conjunts convexos resulten ser prominents, mentre que la noció de semiespai vectorial, apareix com a conseqüència. Les regles de transformació, un aparell dissenyat per connectar les funcions d'ona amb les DF, es defineixen en un proper pas. També es descriuen diversos aspectes d'aquest tipus de discussió preliminar, entre altres el concepte de distribucions d'energia cinètica, que apareixen dins la definició dels espais de Hilbert generals i els espais de Sobolev. Les QSM, com una font de la representació discreta de les estructures moleculars, es fan evidents dins d'aquest concepte. Un desenvolupament posterior de la teoria intenta estudiar els processos de discretització; això és: la transformació dels espais funcionals d'infinites dimensions en espais n-dimensionals. Aquest resultat afegeix noves perspectives a la representació discreta de QO, ja que: a) esdevé una font de nous descriptors, b) descriu el fonament de les QSAR, cosa que permet la construcció de models adequats comWays of developing the formalism where Quantum Similarity Measures (QSM) become a natural product issuing from a specific mathematical framework related to quantum theory are discussed. This fact is used to establish a fundamental connection between Quantum Theory and QSAR, which is analysed in turn within the realm of discrete quantum chemistry. In order to achieve such an objective several theoretical tools are revised in a previous step. The first section is devoted to constructing the concept of the Tagged Set. Next, the definition of Quantum Object (QO) is clarified by means of Quantum Theory background ideas and the previous Tagged Set formalism. In the definition of QO, Density Functions (DF) are shown to play a fundamental role and a possible simplified mathematical picture is presented for possible computational purposes. In the process of preparing the problem-solving tools, convex sets become prominent, and the notion of Vector Semispace appears as a consequence. The Transformation Rule, a device to connect Wavefunctions with DF, is defined in a new step. Various products of this preliminary discussion are described, among them the concept of Kinetic Energy distributions, issuing from the background concept of extended Hilbert and Sobolev spaces. QSM as a source of discrete representation of molecular structures is made evident in this context. Further theoretical development undertakes precise study of discretization, that is, the transformation of infinite-dimensional functional spaces into n-dimensional ones. This result adds new perspectives to the discrete representation of QO, because a) It provides a source of new QO descriptors, b) It describes the QSAR theoretical background enabling the construction of adequate models like tuned-QSAR, and c) It allows the construction of sound and general alternatives of Hammet?s ó or log P parameters. In this context, QSM appear to produce QSAR models constructed with unbiased descriptors, deducible from quant

Molecular rearrangement of an Aza-Scorpiand macrocycle induced by pH: A computational study

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method

Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

<p>Abstract</p> <p>Background</p> <p>Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.</p> <p>Methods</p> <p>A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and <it>de novo </it>molecular design.</p> <p>Results</p> <p>Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified <it>in silico </it>and tested <it>in vitro</it>; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.</p> <p>Conclusions</p> <p>Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for <it>de novo </it>design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.</p

Across chiral and achiral worlds : statistical validation in VCD and explorations in momentum space

This work covers two very different topics, which differ by one main characteristic: chirality. This is a geometric property an object possesses when its mirror image is not superimposable with itself. It is highly important in chemistry and the source of Vibrational Circular Dichroism, the first of the two subjects. As for many spectroscopic techniques, comparing theory with experiment can be a non-trivial task. The aim of this work is to aid with this process using a statistical scheme. A second topic covers the abstract realm of momentum space electron densities, which are - in contrast to the former topic - achiral distributions. They can be experimentally probed using Compton Scattering experiments and Electron Momentum Spectroscopy, revealing a fundamentally different point of view to approach chemistry

Multi-tier framework for the inferential measurement and data-driven modeling

A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic

From bioactive natural products to drug-like small molecules

Natural products have historically been the most productive source of leads for the development of drugs. Thanks to the chemical methodologies of natural products, a vast array of bioactive secondary metabolites from terrestrial and marine sources has been discovered. Many of these natural products became current drug candidates. Therefore, the research of new biologically active compounds through structure elucidation and biological tests is the central issue of these studies. My research is placed in this field and it has been mainly devoted to the discovery and to the chemical and pharmacological investigation of new bioactive natural products as “lead compounds” in the antitumor and antimalarial activities area. My work, described in this PhD thesis, was organized in two different topics: i) isolation and structural characterization of bioactive secondary metabolites from marine invertebrates, ii) synthesis of thiazinoquinones derivatives endowed with cytotoxic and antiplasmodial activities from marine natural metabolites. The achievement of my research project required isolation and extraction procedures. The chemical characterization of the isolated compounds has been performed through an extensive spectroscopic analysis (UV, IR, ECD, 1D and 2D NMR) together with mass spectrometry, computational and electrochemistry methods. I have also used synthetic methods both for the chemical derivatization of the isolated molecules and for the preparation of analogues on the simplified model of natural molecules. During the course of my PhD research, whose results are reported in the following thesis, I have been dealing with the extraction and the chemical re-investigation of a new collection of the Mediterranean ascidian Phallusia fumigata. This analysis led to the isolation of one sulfated sterol, phallusiasterol C, which is a possible modulator of the PXR nuclear receptor. Morover, I have been strongly involved in completing the stereochemistry assignment of phosphoeleganin, a complex acyclic marine natural product, isolated previously from the Mediterranean ascidian Sidnyum elegans. In addition, the electrochemical response of four natural cytotoxic thiazinoquinones, beforehand isolated and characterized from Aplidium species, has been investigated, in order to clarify the mechanism of action which is the basis of their cytotoxicity. The research for new antiplasmodial hits is another main topic of my PhD activity discussed in this thesis. Previously, having identified the thiazinoquinone nucleus as new active chemiotype against P. falciparum, my research started from the development of two new series of methoxy and amide derivatives inspired by two marine metabolites isolated from the Mediterranean ascidian Aplidium conicum. Recently, in order to refine this pharmacophore model and improve the pharmacokinetic and pharmacodynamic properties, I have performed a rational design and synthesis of new modified analogues with simplified side chains and different substituents. In collaboration with the Department of Biomedical, Surgical and Dental sciences (University of Milan), the synthetic analogues of natural quinones have been tested for their in vitro antiplasmodial activity against both chloroquine (CQ)-sensitive (D10) and -resistant (W2) strains of P. falciparum, although some of them were strongly cytotoxic. Some of the synthetic derivatives showed significant antiplasmodial activity together with some important structural requirements. Additionally, in order to rationalize the structure-activity relationships (SARs), an integrated approach based on computational and electrochemistry studies was performed. These studies were carried out by a further collaborating external research group. The above results clearly evidence that quinone natural products represent an excellent source of novel “drug-like” small molecules for drug discovery in antimalarial research