Heuristic molecular lipophilicity potential for computer-aided rational drug design

In my thesis research, I suggest a heuristic molecular lipophilicity potential (HMLP), a structure-based technique requiring no empirical indices of atomic lipophilicity, for computer-aided drug design. The input data used in this approach are molecular geometries and molecular surfaces. The HMLP is a modified electrostatic potential, combined with the averaged influences from the molecular environment. Quantum mechanics is used in calculating the electron density function ñ(r) and the electrostatic potential V(r), and from this information a lipophilicity potential L(r) is generated. The HMLP is a unified lipophilicity and hydrophilicity potential. The interactions of dipole and multipole moments, hydrogen bonds, and charged atoms in molecules are included in the hydrophilic interactions in this model. The HMLP is used to study hydrogen bonds and water-octanol partition coefficients in several examples. The calculated results show that HMLP gives qualitatively and quantitatively correct, as well as chemically reasonable results in cases where comparisons are available. These comparisons indicate that the HMLP has advantages over the empirical lipophilicity potential in many aspects. Three possible screening functions and parameters used in them are tested and optimized in this research. The power screening function, bi||Ri - r||ã, and the exponential screening function, biexp(-||Ri - r|| / d0), give satisfactory results. A new strategy for drug design and combinatory chemistry is presented based on HMLP, and is used in the study of a small molecular system, pyrazole and its derivatives. The mechanism of inhibition of LADH caused by pyrazole and its derivatives is explained based on the calculation results of HMLP indices. Good results are achieved in this example. Further improvements of screening function and visualization of HMLP by computer graphics are discussed. I suggest two possible visualization approaches of HMLP: a two-color system and a three-color system. Their possible applications are discussed. HMLP is suggested as a potential tool in computer-aided three-dimensional drug design, studies of 3D-QSAR, active structure of proteins, and other types of molecular interactions

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

Rational methods for the selection of diverse screening compounds.

Traditionally a pursuit of large pharmaceutical companies, high-throughput screening assays are becoming increasingly common within academic and government laboratories. This shift has been instrumental in enabling projects that have not been commercially viable, such as chemical probe discovery and screening against high-risk targets. Once an assay has been prepared and validated, it must be fed with screening compounds. Crafting a successful collection of small molecules for screening poses a significant challenge. An optimized collection will minimize false positives while maximizing hit rates of compounds that are amenable to lead generation and optimization. Without due consideration of the relevant protein targets and the downstream screening assays, compound filtering and selection can fail to explore the great extent of chemical diversity and eschew valuable novelty. Herein, we discuss the different factors to be considered and methods that may be employed when assembling a structurally diverse compound collection for screening. Rational methods for selecting diverse chemical libraries are essential for their effective use in high-throughput screens.We are grateful for financial support from the MRC, Wellcome Trust, CRUK, EPSRC, BBSRC and Newman Trust.This is the author accepted manuscript. The final version is available from American Chemical Society via http://dx.doi.org/10.1021/cb100420

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

Novel Ligand-Based Approach to Screening of Large Databases for Paramphistomicide Lead Generation

In this report, non-stochastic and stochastic 2D atom-based linear indices were used to the discrimination of paramphistomicide compounds from inactive ones. Two linear classification-based QSAR models were obtained. These equations, performed considering both non-stochastic and stochastic TOMOCOMD-CARDD descriptors, classify correctly 88.57% of chemicals in database, for a good Mathew’s correlation coefficient of 0.77. A few anthelmintics compounds and other drugs from the Merck Index, Negwer handbook, and Goodman & Gilman were selected/identified by the models as possible paramphistomicide, one of them was found in the recent literature as possessing this activity. The results demonstrate the usefulness of TOMOCOMD-CARDD method for drug discovery of new lead paramphistomicide compounds.En este informe se emplearon índices lineales estocásticos y no estocásticos en 2D, basados en átomos, para discriminar los compuestos de acción paramfistomicida de los inactivos. Se obtuvieron dos modelos lineales QSAR basados en la clasificación. Estas ecuaciones, llevadas a cabo teniendo en cuenta descriptores TOMOCOMD-CARDD tanto estocásticos como no estocásticos, clasifican correctamente el 88,57% de los elementos químicos de la base de datos, arrojando un buen coeficiente de correlación de Mathews del 0,77. Los modelos seleccionaron/identificaron algunos compuestos antihelmínticos y otros fármacos del índice Merck, del manual Negwer y de Goodman & Gilman como posibles paramfistomicidas, y la literatura reciente incluye a uno de ellos como poseedor de esta actividad. Los resultados demuestran la utilidad del método TOMOCOMD-CARDD para el descubrimiento de fármacos y de nuevos compuestos líderes de acción paramfistomicida.Ciencias Experimentale

Information Architecture for a Chemical Modeling Knowledge Graph

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes and 4 million edges and can be explored with interactive visualization software. The structure of the property graph is centered around models and molecules which enables efficient and intuitive inter- and intra-model evaluation. We use a curated lipophilicity dataset to demonstrate graph use cases. Difficult to predict molecules were identified across multiple models simultaneously. Powerful and expressive graph queries were implemented to identify molecular fragments that were both prevalent and associated with high lipophilicity prediction error

Investigation of hydro-lipophilic properties of n-alkoxyphenylhydroxynaphthalenecarboxamides

The evaluation of the lipophilic characteristics of biologically active agents is indispensable for the rational design of ADMET-tailored structure–activity models. N-Alkoxy-3-hydroxynaphthalene-2-carboxanilides, N-alkoxy-1-hydroxynaphthalene-2-carboxanilides, and N-alkoxy-2-hydroxynaphthalene-1-carboxanilides were recently reported as a series of compounds with antimycobacterial, antibacterial, and herbicidal activity. As it was found that the lipophilicity of these biologically active agents determines their activity, the hydro-lipophilic properties of all three series were investigated in this study. All 57 anilides were analyzed using the reversed-phase high-performance liquid chromatography method for the measurement of lipophilicity. The procedure was performed under isocratic conditions with methanol as an organic modifier in the mobile phase using an end-capped non-polar C18 stationary reversed-phase column. In the present study, a range of software lipophilicity predictors for the estimation of clogP values of a set of N-alkoxyphenylhydroxynaphthalenecarboxamides was employed and subsequently cross-compared with experimental parameters. Thus, the empirical values of lipophilicity (logk) and the distributive parameters (π) were compared with the corresponding in silico characteristics that were calculated using alternative methods for deducing the lipophilic features. To scrutinize (dis)similarities between the derivatives, a PCA procedure was applied to visualize the major differences in the performance of molecules with respect to their lipophilic profile, molecular weight, and violations of Lipinski’s Rule of Five

Taking model pursuit seriously

This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure-Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis

Rationality in discovery : a study of logic, cognition, computation and neuropharmacology

Part I Introduction The specific problem adressed in this thesis is: what is the rational use of theory and experiment in the process of scientific discovery, in theory and in the practice of drug research for Parkinson’s disease? The thesis aims to answer the following specific questions: what is: 1) the structure of a theory?; 2) the process of scientific reasoning?; 3) the route between theory and experiment? In the first part I further discuss issues about rationality in science as introduction to part II, and I present an overview of my case-study of neuropharmacology, for which I interviewed researchers from the Groningen Pharmacy Department, as an introduction to part III. Part II Discovery In this part I discuss three theoretical models of scientific discovery according to studies in the fields of Logic, Cognition, and Computation. In those fields the structure of a theory is respectively explicated as: a set of sentences; a set of associated memory chunks; and as a computer program that can generate the observed data. Rationality in discovery is characterized by: finding axioms that imply observation sentences; heuristic search for a hypothesis, as part of problem solving, by applying memory chunks and production rules that represent skill; and finding the shortest program that generates the data, respectively. I further argue that reasoning in discovery includes logical fallacies, which are neccesary to introduce new hypotheses. I also argue that, while human subjects often make errors in hypothesis evaluation tasks from a logical perspective, these evaluations are rational given a probabilistic interpretation. Part III Neuropharmacology In this last part I discusses my case-study and a model of discovery in a practice of drug research for Parkinson’s disease. I discuss the dopamine theory of Parkinson’s disease and model its structure as a qualitative differential equation. Then I discuss the use and reasons for particular experiments to both test a drug and explore the function of the brain. I describe different kinds of problems in drug research leading to a discovery. Based on that description I distinguish three kinds of reasoning tasks in discovery, inference to: the best explanation, the best prediction and the best intervention. I further demonstrate how a part of reasoning in neuropharmacology can be computationally modeled as qualitative reasoning, and aided by a computer supported discovery system