Artificial intelligence for porous organic cages

Porous organic cages are a novel class of molecules with many promising applications, including in separation, sensing, catalysis and gas storage. Despite great promise, discovery of these materials is hampered by a lack of computational tools for exploring their chemical space, and significant expense associated with prediction of their properties. This results in significant synthetic effort being directed toward molecules which do not have targeted properties. This thesis presents multiple computational tools which can aid the discovery and design of these materials by increasing the number of synthetic candidates which are likely to exhibit desired, targeted properties. Firstly, a broadly applicable methodology for the construction of computational models of materials is presented. This facilitates the automated modelling and screening of materials that would otherwise have to be carried out in a more labour-intensive way. Secondly, an evolutionary algorithm is implemented and applied to the design of porous organic cages. The algorithm is capable of producing cages closely matching user-defined design criteria, and its implementation is designed to allow future applications in other fields of material design. Finally, machine learning is used to accurately predict properties of porous organic cages, orders of magnitude faster than has been possible with traditional, simulation-based approaches.Open Acces

A treatment of stereochemistry in computer aided organic synthesis

This thesis describes the author’s contributions to a new stereochemical processing module constructed for the ARChem retrosynthesis program. The purpose of the module is to add the ability to perform enantioselective and diastereoselective retrosynthetic disconnections and generate appropriate precursor molecules. The module uses evidence based rules generated from a large database of literature reactions. Chapter 1 provides an introduction and critical review of the published body of work for computer aided synthesis design. The role of computer perception of key structural features (rings, functions groups etc.) and the construction and use of reaction transforms for generating precursors is discussed. Emphasis is also given to the application of strategies in retrosynthetic analysis. The availability of large reaction databases has enabled a new generation of retrosynthesis design programs to be developed that use automatically generated transforms assembled from published reactions. A brief description of the transform generation method employed by ARChem is given. Chapter 2 describes the algorithms devised by the author for handling the computer recognition and representation of the stereochemical features found in molecule and reaction scheme diagrams. The approach is generalised and uses flexible recognition patterns to transform information found in chemical diagrams into concise stereo descriptors for computer processing. An algorithm for efficiently comparing and classifying pairs of stereo descriptors is described. This algorithm is central for solving the stereochemical constraints in a variety of substructure matching problems addressed in chapter 3. The concise representation of reactions and transform rules as hyperstructure graphs is described. Chapter 3 is concerned with the efficient and reliable detection of stereochemical symmetry in both molecules, reactions and rules. A novel symmetry perception algorithm, based on a constraints satisfaction problem (CSP) solver, is described. The use of a CSP solver to implement an isomorph‐free matching algorithm for stereochemical substructure matching is detailed. The prime function of this algorithm is to seek out unique retron locations in target molecules and then to generate precursor molecules without duplications due to symmetry. Novel algorithms for classifying asymmetric, pseudo‐asymmetric and symmetric stereocentres; meso, centro, and C2 symmetric molecules; and the stereotopicity of trigonal (sp2) centres are described. Chapter 4 introduces and formalises the annotated structural language used to create both retrosynthetic rules and the patterns used for functional group recognition. A novel functional group recognition package is described along with its use to detect important electronic features such as electron‐withdrawing or donating groups and leaving groups. The functional groups and electronic features are used as constraints in retron rules to improve transform relevance. Chapter 5 details the approach taken to design detailed stereoselective and substrate controlled transforms from organised hierarchies of rules. The rules employ a rich set of constraints annotations that concisely describe the keying retrons. The application of the transforms for collating evidence based scoring parameters from published reaction examples is described. A survey of available reaction databases and the techniques for mining stereoselective reactions is demonstrated. A data mining tool was developed for finding the best reputable stereoselective reaction types for coding as transforms. For various reasons it was not possible during the research period to fully integrate this work with the ARChem program. Instead, Chapter 6 introduces a novel one‐step retrosynthesis module to test the developed transforms. The retrosynthesis algorithms use the organisation of the transform rule hierarchy to efficiently locate the best retron matches using all applicable stereoselective transforms. This module was tested using a small set of selected target molecules and the generated routes were ranked using a series of measured parameters including: stereocentre clearance and bond cleavage; example reputation; estimated stereoselectivity with reliability; and evidence of tolerated functional groups. In addition a method for detecting regioselectivity issues is presented. This work presents a number of algorithms using common set and graph theory operations and notations. Appendix A lists the set theory symbols and meanings. Appendix B summarises and defines the common graph theory terminology used throughout this thesis

Organic pi-stacking Semiconducting Material: Design, Synthesis and the Analysis of Structure and Properties

Organic semiconducting materials have been under intensive investigation in the recent decades for potential applications in various electronic or optoelectronic devices such as light emitting diodes, photovoltaic cells and field effect transistors. Compared to inorganic counterparts, organic charge transport materials are attractive for their abilities of forming thin-films, large area manufacturing, compatibility with flexible substrate, light weight and potential low fabrication cost. The charge transport property of the organic active layer is one of the key factors to the electronic or optoelectronic performance of devices. Research projects presented in this thesis focused on improving charge carrier mobility of organic charge transport materials as it is a property determined by the hierarchical structure of the material. Strong effort has been made to the design of advanced molecular structures and controlling self-assembly behaviors. Chapter 1 introduces the general background of charge transport materials, including: the nature of charge transport in organic semiconducting materials, three widely used methods for charge carrier mobility measurements and the current development of organic charge transport materials. Advantages and drawbacks in applications were analyzed with ordered and disordered organic systems. A more thorough review was given to the engineering and the application of the discotic columnar liquid crystalline (DCLC) phase. Chapter 2 describes a DCLC phase with a novel hierarchical structure in which each supra-molecular column features a bundled-stack structure. The molecular design rationale was explained and the thermal behavior and phase structure were characterized. Charge carrier mobility of compound 1 was measured to be 0.05 cm2V-1s-1 with pulse radiolysis time-resolved microwave conductivity. The incorporation of the bundled stack structure may potentially be a fundamental solution towards enhancing the organic semiconductor\u27s electronic performance. Chapter 3 introduces three chain functionalized perylene tetracarboxylic monoimide diester derivatives (PEIs) with monotropic DCLC phases. The intra-column rotation angle was determined to be 60 o between neighboring PEI molecules, which is a substantial improvement of the transfer integral compared to the perylene tetracarboxylic diimides with a 90 o rotation angle. The rotation angle was further tuned by incorporating branched aliphatic substitution to the PEI core as described in chapter 4. By reducing the length of the alkyl swallow tail, the rotation angle changes from 60 o to 72 o which is even more favorable to the electronic coupling between neighboring PEI units. Through those studies, we have shown that the engineering of DCLC phase may lead to substantial improvements on charge transport properties of organic semiconducting materials.

Simulation, Application, and Resilience of an Organic Neuromorphic Architecture, Made with Organic Bistable Devices and Organic Field Effect Transistors

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at University of Denver. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the inorganic electronics in the form of physical flexibility and low cost of fabrication. However, their device variability between individual devices is also much greater. Therefore the second major contribution of this thesis is the analysis of resilience of neural networks subjected to physical damage and other manufacturing faults

Synthesis, molecular and solid state structures, and magnetic properties of sandwich lanthanide phthalocyanines lacking C-H bonds

A new class of sandwich phthalocyanine (Pc) compounds without C-H bonds was synthesized and characterized. They are the bis[octakis(perfluoro i-C3F7) octakis(perfluoro)phthalocyaninato(2−)]M(III) complexes, formulated as (F64Pc)2MH, M = Tb, Dy, Lu, Y. Single molecular magnetic (SMM) behavior in the (F64Pc)2TbH and (F64Pc)2DyH complexes was confirmed through slowed relaxation response of their magnetization in an applied time varying magnetic field during alternating current (AC) magnetic testing in the range 2 - 50 K. The energy barrier to magnetic reversal, Δ = 215 cm-1 and the pre-exponential factor, τ0-1 = 2.7 x 107-1 were estimated for the undiluted (F64Pc)2TbH with direct current bias magnetic field (Hdc) of 1000 Oe applied. Undiluted (F64Pc)2DyH gave estimated values of Δ = 31 cm-1 and τ0-1 = 1.6 x 105 s-1 at Hdc = 500 Oe. These respective values are within the range observed for the unsubstituted Tb and Dy analogues. Magnetic hysteresis testing at 0.04 K exhibited hysteresis in both of these complexes. Magnetic circular dichroism (MCD) tests on (F64Pc)2Tb frozen in acetone at 1.8-1.9 K exhibited a “butterfly shaped” hysteresis which appeared to depend on the oxidation state of the complex and which also provided evidence of quantum tunneling of magnetization. The chemical tuning of the Pc macrocycle of these complexes through the replacement of the H atoms with the electron withdrawing fluorine atom and i-C3F7 group has created additional functionalities. Thermal analysis in the forms of thermogravi metric analysis and differential scanning calorimetry showed the complexes to be stable up to 450°C in air and N2. Cyclic voltammetry revealed four reduced and one oxidized states, the oxidized state occurring at \u3e 1.2 V, showing the complexes’ resistance to oxidation. The (F64Pc)2MH compounds derive their uniqueness as [(F64Pc)2M]− sandwich phthalocyanine complexes with exceptional thermal and oxidative stability

Third Order Nonlinearity Of Organic Molecules

The main goal of this dissertation is to investigate the third-order nonlinearity of organic molecules. This topic contains two aspects: two-photon absorption (2PA) and nonlinear refraction (NLR), which are associated with the imaginary and real part of the third-order nonlinearity (χ (3)) of the material, respectively. With the optical properties tailored through meticulous molecular structure engineering, organic molecules are promising candidates to exhibit large third-order nonlinearities. Both linear (absorption, fluorescence, fluorescence excitation anisotropy) and nonlinear (Z-scan, two-photon fluorescence, pump-probe) techniques are described and utilized to fully characterize the spectroscopic properties of organic molecules in solution or solid-state form. These properties are then analyzed by quantum chemical calculations or other specific quantum mechanical model to understand the origins of the nonlinearities as well as the correlations with their unique molecular structural features. These calculations are performed by collaborators. The 2PA study of organic materials is focused on the structure-2PA property relationships of four groups of dyes with specific molecular design approaches as the following: (1) Acceptor-π-Acceptor dyes for large 2PA cross section, (2) Donor-π-Acceptor dyes for strong solvatochromic effects upon the 2PA spectra, (3) Near-infrared polymethine dyes for a symmetry breaking effect, (4) Sulfur-squaraines vs. oxygen-squaraines to study the role of sulfur atom replacement upon their 2PA spectra. Additionally, the 2PA spectrum of a solid-state single crystal made from a Donor-π-Acceptor dye is measured, and the anisotropic nonlinearity is studied with respect to different incident polarizations. These studies further advance our iv understanding towards an ultimate goal to a predictive capability for the 2PA properties of organic molecules. The NLR study on molecules is focused on the temporal and spectral dispersion of the nonlinear refraction index, n2, of the molecules. Complicated physical mechanisms, originating from either electronic transitions or nuclei movement, are introduced in general. By adopting a prism compressor / stretcher to control the pulsewidth, an evolution of n2 with respect to incident pulsewidth is measured on a simple inorganic molecule –carbon disulfide (CS2) in neat liquid at 700 nm and 1064 nm to demonstrate the pulsewidth dependent nonlinear refraction. The n2 spectra of CS2 and certain organic molecules are measured by femtosecond pulses, which are then analyzed by a 3-level model, a simplified Sum-over-states quantum mechanical model. These studies can serve as a precursor for future NLR investigations

Comparative molecular field analysis (CoMFA) of protonated methylphenidate phenyl-substituted analogs

Protonated methylphenidate (pMP) and several phenyl-substituted pMP analogs were analyzed using Comparative Molecular Field Analysis (CoMFA) to develop a pharmacophore for dopamine transporter (DAT) binding. This research is a part of an interdisciplinary study on using methylphenidate (MP) analogs to block the binding of cocaine to the DAT as a treatment for addiction. A random search conformational analysis using key pMP torsional angles was performed to create conformer families representing possible bioactive conformations. The lowest energy pMP conformer of each family was used as a template to create phenyl-substituted pMP analogs. Partial least squares analysis was used to determine the combination of electrostatic and steric cutoffs that yielded the highest predictability (q 2) . q 2 values above 0.5 were achieved for all conformer families. The best model was used to propose a pharmacophore to predict DAT binding affinity. The results were compared to a previous CoMFA study on neutral MP

Development of Machine Learning Models for Generation and Activity Prediction of the Protein Tyrosine Kinase Inhibitors

The field of computational drug discovery and development continues to grow at a rapid pace, using generative machine learning approaches to present us with solutions to high dimensional and complex problems in drug discovery and design. In this work, we present a platform of Machine Learning based approaches for generation and scoring of novel kinase inhibitor molecules. We utilized a binary Random Forest classification model to develop a Machine Learning based scoring function to evaluate the generated molecules on Kinase Inhibition Likelihood. By training the model on several chemical features of each known kinase inhibitor, we were able to create a metric that captures the differences between a SRC Kinase Inhibitor and a non-SRC Kinase Inhibitor. We implemented the scoring function into a Biased and Unbiased Bayesian Optimization framework to generate molecules based on features of SRC Kinase Inhibitors. We then used similarity metrics such as Tanimoto Similarity to assess their closeness to that of known SRC Kinase Inhibitors. The molecules generated from this experiment demonstrated potential for belonging to the SRC Kinase Inhibitor family though chemical synthesis would be needed to confirm the results. The top molecules generated from the Unbiased and Biased Bayesian Optimization experiments were calculated to respectively have Tanimoto Similarity scores of 0.711 and 0.709 to known SRC Kinase Inhibitors. With calculated Kinase Inhibition Likelihood scores of 0.586 and 0.575, the top molecules generated from the Bayesian Optimization demonstrate a disconnect between the similarity scores to known SRC Kinase Inhibitors and the calculated Kinase Inhibition Likelihood score. It was found that implementing a bias into the Bayesian Optimization process had little effect on the quality of generated molecules. In addition, several molecules generated from the Bayesian Optimization process were sent to the School of Pharmacy for chemical synthesis which gives the experiment more concrete results. The results of this study demonstrated that generating molecules throughBayesian Optimization techniques could aid in the generation of molecules for a specific kinase family, but further expansions of the techniques would be needed for substantial results