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

Towards Improving The Accuracy of Implicit Solvent Models and Understanding Electrostatic Catalysis in Complex Solvent Environment

This thesis develops improved protocols for studying reactions in solution and uses them to explore the possibility of harnessing complex non-standard solvent environments to catalyse chemical reactions. The thesis covers different but related topics: Improving the accuracy of implicit solvent models. Implicit solvent models are simple cost-effective strategies for modelling solvent as a polarizable continuum. However, the accuracy of this approach can be quite variable. Herein, we examine approaches to improving their accuracy through cavity scaling, the choice of theoretical level and the inclusion of explicit solvent molecules. For SMD, we show that the best performance is achieved when cavity scaling is not employed, while for PCM we present a series of electrostatic scale factors that are radii, solvent and ion type dependent. For both families of method, we also highlight the importance choosing an appropriate level of theory, and identify when explicit solvent molecules are required.. Modelling electrostatic catalysis in complex solvent environment. Recent nanoscale experiments have shown that electric fields are capable of catalysing and controlling chemical reactions, but experimental platforms for scaling these effects remain elusive. Herein, two different approaches to addressing this challenge are explored. The first is using the internal electric field of ordered solvents and ionic liquids, the second is using the electric fields that form naturally at the gas-water interface. A multi-scale modelling approach was developed using polarizable force field based molecular dynamic simulation, post-HF, DFT and semi-empirical quantum chemical calculations. We showed that after exposure to an external electric field, ensembles of solvent or ionic liquid molecules become ordered and this ordering can generate an internal electric field, which persists even after the external potential is removed. Experimental collaborators subsequently detected this field as an open-circuit potential that is strong and long-lived. Computationally we showed that this field is enough to lower reaction barriers by as much as 20 kcal mol-1, and we also developed a predictive structure-reactivity model to choose ionic liquids that optimize this field. In the second approach, we harnessed the electric fields of the gas-water interface. A collaborator showed that in the presence of static, inert gas bubbles, the oxidation potential of HO anion/HO radical was dramatically lowered (by more than 0.5V), much more than any subtle concentration effects predicted by the Nernst equation. Further experiments showed that a high unbalanced concentration of HO- ions (as much as 5M) accumulate at the interface. Our multi-scale modelling calculations showed that this reduction in potential was due to the mutual repulsion of the HO- ions and as little as 1M unbalanced excess was enough to explain the experimental results. The work raises opportunities in reducing the cost of electrochemical processes, and points to electrostatic effects contributing to the well-known catalytic effects of "on water" reactions. Works in this thesis are expected to be useful in the future studies of solution-phase pKa, redox potential, electrostatic catalysis and ionic liquids-based electrochemical devices

Multi-scale modelling of proto-zeolitic solutions

A number of aspects of the pre-nucleation zeolite synthesis solution are considered. Various environmental and structural e�ects on the 29Si NMR chemical shift of silicon nuclei are investigated, in order to ascertain the necessary computational model for a systematic study of oligomeric silicate species identi�ed or postulated to be present during this phase of zeolite crystal growth. It is demonstrated that, using a model with the oligomer in a fully protonated state and including an implicit representation of the solvent, a reasonably inexpensive model can provide good agreement with experimental results. The systematic study of 59 oligomers reveals several cases for re-assignment of experimentally observed peaks, as well as providing assistance in cases where full assignment has proved impossible from experiment. The e�ects of structure directing agents (SDAs), commonly used in zeolite synthesis, are investigated. Using ab initio molecular dynamics (AIMD) to simulate the SDA in the presence of water and a speci�cally designed computer code, it is demonstrated how SDAs lead to the formation of various rings of water in their hydration layer. Furthermore, di�erent properties of the SDAs are shown to induce the formation of di�erent distributions of the various sizes of rings. It is found that certain SDAs result in the formation of clusters of water rings in a network which is isomorphic with some of the zeolite frameworks for which they are know to direct. Finally a new inter-atomic potential is developed for modelling silicate clusters, in order to allow longer simulations of larger systems than are accessible using AIMD methods. This potential is then used to simulate two cage-like silicate oligomers surrounded by water. In these simulations layers of ordered water, similar to those found at zeolite crystal surfaces, are found. These �ndings have implications for the understanding of the aggregation of oligomeric species prior to nucleation. This work was generously supported by the Engineering and Physical Science Research Council. The �nal chapter was also made possible by a Junior Research Fellowship from the Thomas Young Centre. The simulation presented in chapter 4 were performed on the HPCx supercomputer and UCL's Legion supercomputer

Identifying optimal solvents for reactions using quantum mechanics and computer-aided molecular design

A new iterative hybrid methodology, incorporating quantum mechanics (QM) calculations and a computationally inexpensive computer-aided molecular design (CAMD) methodology, QM-CAMD, for identification of optimal solvents for reactions is presented. The methodology has been applied to a Menschutkin reaction, where pyridine and phenacyl bromide are the reactants. The QM calculations take on the form of density functional theory calculations with a given solvent treated using continuum solvation models. The accuracy of the solvent QM calculations is assessed by computing free energies of solvation for different solvation models; the IEF-PCM, SM8 and SMD models are studied and SMD is identified as the best model. Rate constants kQM, determined from QM calculations, are calculated based on conventional transition state theory (Eyring 1935, Evans & Polanyi 1935). By using the SMD solvation model and a statistical mechanics derivation of kQM, rate constant predictions within an order of magnitude are achieved. For a small set of solvents investigated by QM, selected solvent properties are predicted using group contribution (GC) methods. 38 structural groups are considered in this approach. The QM-computed rate constants and solvent properties determined by GC are used to obtain a computationally inexpensive reaction model, based on an empirical linear free energy relationship, which is used to predict reaction rate constants. This predictive reaction model is incorporated into an optimisation-based CAMD methodology. With an objective function of maximising the reaction rate constant subject to molecular and reaction condition constraints, optimal solvent candidates are identified. By considering a design space of over 1000 solvent molecules, solvent candidates containing nitro-groups are predicted to be optimal for the Menschutkin reaction. This outcome supports experimental results for a related reaction available in the literature (Lassau & Jungers 1968). For verification purposes, Ganase et al. (2011) have measured (based on 1H NMR data and kinetic analysis) the rate constant for the reaction of interest in a number of solvents and report a significant increase in the rate constant with nitromethane as the solvent

The explicit polarization theory as a quantum mechanical force field and the development of coarse-grained models for simulating crowded systems of many proteins

University of Minnesota Ph.D. dissertation. January 2014. Major: Scientific Computation. Advisor: Prof. Jiali Gao. 1 computer file (PDF); xxviii, 274 pages, appendices A-B.This dissertation consists of two parts. The first part concerns the use of explicit polarization theory (X-Pol), the semiempirical polarized molecular orbital (PMO) method, and the dipole preserving, polarization consistent (DPPC) charge model as a quantum mechanical force field (QMFF). A detailed discussion of Hartree-Fock theory and X-Pol is provided, along with expressions for the energy and the analytical first derivative of this QMFF. Test cases for this QMFF with extensive comparisons to experimental data and other models are provided for water (XP3P) and hydrogen fluoride (XPHF), showing that the PMO/X-Pol/DPPC approach discussed in this dissertation is competitive with the most accurate models for those two chemical species over a wide range of chemical and physical properties.The second part of this dissertation concerns the development and application of coarse-grained models for protein dynamics. First, a coarse-grained force field (CGFF) for macromolecules in crowded environments is introduced and described along with a visualization environment for the cartoon-like rendering of biomolecules in vivo. This CGFF is tested against experimental diffusion coefficients for myoglobin (Mb) at a wide range of concentrations, including volume fractions as high as 40%, finding it to be surprisingly accurate for its simplicity and level of coarseness. Second, an analytical coarse-grained (ACG) model for mapping the internal dynamics of proteins into a spherical harmonic expansion is described

A COMPUTATIONAL STUDY ON BIOCHEMICAL REACTIONS OF SULFUR-CONTAINING COMPOUNDS

Ph.DDOCTOR OF PHILOSOPH

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here