Automated in Silico Design of Homogeneous Catalysts

Catalyst discovery is increasingly relying on computational chemistry, and many of the computational tools are currently being automated. The state of this automation and the degree to which it may contribute to speeding up development of catalysts are the subject of this Perspective. We also consider the main challenges associated with automated catalyst design, in particular the generation of promising and chemically realistic candidates, the tradeoff between accuracy and cost in estimating the catalytic performance, the opportunities associated with automated generation and use of large amounts of data, and even how to define the objectives of catalyst design. Throughout the Perspective, we take a cross-disciplinary approach and evaluate the potential of methods and experiences from fields other than homogeneous catalysis. Finally, we provide an overview of software packages available for automated in silico design of homogeneous catalysts.publishedVersio

CatSD: structural database and high-throughout predictive workflows for homogeneous catalyst design

Identification of highly active catalysts is an important process across multiple industries including drug development, process chemistry and agrochemicals. The lack of understanding of ligand properties and catalytic pathways are limiting factors for the uptake of more sustainable and highly active catalysts. Herein we report a novel method for the identification of ligands and the prediction of their activity for homogeneous catalysts from the Cambridge Structural Database. We present CatSD, a structural database complete with catalytically relevant features to enable the mining of organometallic ligands from the CSD. We also present a high-throughput computational workflow for the prediction of activation energies and mechanistic exploration. This workflow is on a timescale similar to experimental high-throughput screening and provides energies with an accuracy of 3.9 kcal mol-1 . CatSD and the prediction workflow were applied to the Ullmann-Goldberg reaction to identify novel ligands for amine and amide coupling partners. Over 10,000 ligands were identified from the CSD for both coupling partners. The workflow showed excellent reliability for the generation of starting structures (99.7%) and good reliability for the optimisation of important intermediates (>84%) and transition states (TSOA: 33-61%, TSSig: 83-85%). Several ligands were validated experimentally identifying a previously unreported active ligand class. The effect of ligand properties was explored using machine learning to identify several key characteristics for both nucleophile coupling partners. Machine learning was also used to predict activation energies without the need to calculate the transition state. Models were optimised providing accuracy on par with the accuracy of the workflow calculations. It is our hope that the methodologies presented in this work will aid the discovery and design of ligands for homogeneous catalysts for the wider chemistry community as well as stimulate further research in this field

Machine learning activation energies of chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Structure-activity investigation on laccases by computational and site directed mutagenesis studies

Laccases belong to multi copper oxidase enzyme family (EC 1.10.3.2). Their capacity to oxidíze a wide range of substrates makes them very attractive for the industry and are growing in importance for environmentally-friendly synthesis. Laccases have three different copper sites including, type 1 (T1), type 2 (T2) and type 3 (T3). The function of the T1 site is shuttling electrons from the substrate to the trinuclear copper cluster. During the catalytic cycle of laccase, four electrons are removed from four substrate molecules, which are finally transferred to reduce oxygen to two water molecules .Comparison of the kinetic parameters using several laccases and several substrates reveals that the reaction rate of laccase correlates with the redox potential difference between the T1 copper and the substrate. In recent years, the demonstrated potential of laccases in a range of applications has motivated the progress of laccase engineering efforts. Computational simulations can reveal targets for protein engineering to be explored by site-directed mutagenesis (or semi-rational approaches). In this work we used computational methods for studying interaction of different substrates with laccases and structural activity of the enzyme. The goal of the present study was to characterize the laccase binding pocket of fungal and bacterial laccases in order to establish their common pharmacophoríc characteristics. For this purpose, we first performed molecular docking studies to identify those residues involved in the interaction with diverse substrates. Our results indicate that bacterial laccase {1UVW) has less hydrophobic and aromatic residues in the activity site in comparison to other fugal structures of this study, as a result, find a pose that interacts with residues needs more energy. Subsequently, we evaluated the effect of protonation state of a conserved residue in fungal laccase, Asp/Glu, through molecular dynamics simulation. In a subsequent step, we applied QMMM-2QM-MD approach for one of the fungal laccase structure (3FU8) for calculating redox potential value. The result indicates that the difference in redox potentials changes from 7-17 to 74-92 kJ/mol if the redox state of T1Cu and DMP in the other subunit change and we correctly predict that CuT1ox/DMPred state is more stable than the CuT1red/DMPox state. After the insight gathered from computational studies we started site directed mutagenesis studies on two residues of the binding pocket in order to find their effect on the redox potential value. We made a combinatorial library for position 192 and 296 in MtlL T2. The clone contained A192P and L296W (3H12) mutation and clone contained A192P and 296L {19G8) showed activity with violuríc acid 1.23 and 1.33 fold higher than parental type, respectively. Moreover, the clone contained A192R and L296W (15H11) and clone with mutation A192R and L296L {5B4) showed higher activity with molybdenum compound in comparison to parental type. After experimental characterization of the 19G8 and 5B4 mutants, we studied the structural changes produced in the binding pocket. For this purpose we generated a three-dimensional structure of the two mutants using M.albomices laccase as template by homology modelling. Whereas the former mutant exhibits a similar binding pocket to the template, the latter appears to be smaller. In any case, subsequent docking studies did not show any differential behaviour and ligands could bind to both binding pockets in a similar way. Finally, we calculated the redox potential of the mutant A296L MaL that is similar to the former mutant, yielding a value of 167 kJ/mol. This is higher than the value obtained for MalL supporting the effect of this mutation on the redox potential.Las lacasas pertenecen a la familia de enzimas multícobre oxidadas (EC 1.10.3.2). Su capacidad de oxidar una amplia gama de sustratos las hace muy atractivas para la industria y su utilización está creciendo en importancia para la síntesis respetuosa del medio ambiente. Las lacasas tienen tres tipos diferentes de cobre: tipo 1 (T1), Tipo 2 (T2) y típo 3 (T3). La función del sitio de T1 es la de transportar electrones desde el sustrato al clúster de cobre trinuclear. Durante el ciclo catalítico de la lacasa, cuatro electrones son transferidos desde cuatro moléculas de sustrato para reducir oxígeno a dos moléculas de agua. La comparación de los parámetros cinéticos utilizando varias lacasas y varios sustratos revela que la velocidad de reacción de la lacasa se correlaciona con la diferencia de potencial redox entre el cobre T1 y el sustrato. En los últimos años, el potencial demostrado por las lacasas en una gama de aplicaciones ha motivado el progreso en la ingeniería de lacasas. Las simulaciones computacionales pueden revelar residuos clave que pueden ser cambiados por mutagénesís dirigida (o enfoques semi-racionales). En este trabajo se han utilizado métodos computacionales para el estudio de la interacción de diferentes sustratos con lacasas y ver su efecto sobre la actividad. El objetivo del presente estudio fue caracterizar la unión de lacasa bolsillo de lacasas fúngícas y bacterianas con el fin de establecer sus características farmacofórícas comunes. Para este propósito, hemos realizado estudios de anclaje moleculares para identificar aquellos residuos que participan en la interacción con diversos substratos. Nuestros resultados indican que la lacasa bacteriana (1UVN) tiene un número menor de residuos hidrófobos y aromáticos que las estructuras fúngicas, como consecuencia la unión no es tan fuerte. Posteriormente, se evaluó el efecto del estado de protonación de un residuo Asp / Glu conservado en lacasas fúngicas a través de dinámica molecular. En una etapa posterior, se aplicó enfoque QMMM-2QM-MD para uno de la estructura lacasa fúngica (3FU8) para calcular el valor potencial redox. El resultado índica que la diferencia en los potenciales redox cambios 7-17 a 74-92 kJ/mol sí el estado redox de T1Cu y DMP en la otra subunidad cambio y correctamente predecir qué estado CuT1ox / DMPred es más estable que el CuT1red / estado DMPox. Después de los estudios computacionales se llevó a cabo un estudio de mutagénesis dirigida sobre dos residuos del bolsillo de unión, con el fin de encontrar su efecto sobre el valor potencial redox. Con este objetivo se llevó a cabo una biblioteca combinatoria para la posición 192 y 296 en MtL T2. El clan contenía A192P y L296W (3H 12) y el clan contenía la mutación A192P y L296L (19G8) mostraron una actividad con ácido violurico 1,23 y 1,33 veces mayor que la de tipo parental, respectivamente. Por otra parte, el clon contenía A192R y L296W (15h11) y el clon con A192R mutación y L296L (5B4) mostraron una mayor actividad con el compuesto de molibdeno en comparación con el tipo parental. Después de la caracterización experimental de los mutantes 19G8 y 5B4, estudiamos los cambios estructurales que se producen en el bolsillo de unión. Con este fin generamos una estructura tridimensional de los dos mutantes utilizando la lacasa de M.albomices como plantilla, por medio de la modelización por homología. Mientras que el primer mutante exhibe un bolsillo de unión similar al de la plantilla, éste es más pequeño en el segundo mutante. En cualquier caso, los estudios de anclaje molecular posteriores no mostraron ningún comportamiento diferencial y los ligandos podrían unirse a los dos bolsillos de unión de una manera similar. Finalmente, se calculó el potencial redox de la mutante A296L MaL que es similar al mutante 19G8, obteniéndose un valor de 167 kJ/mol. Este valor es más alto que el obtenido para MaL, apoyando el efecto que tiene esta mutacíón sobre el potencial redox

The use of ligand field molecular mechanics and related tools in the design of novel spin crossover complexes

The aim of the work presented in this thesis is to explore computational approaches to the modelling and discovery of spin crossover (SCO) transition metal complexes. Both ‘ab initio’ methods, based mainly on density functional theory, and empirical force fields based on ligand field molecular mechanics (LFMM) have been considered. It is shown that whilst a user can choose a functional and basis set combination through validation to experimental data which will yield accurate results for a series of related systems this combination is not necessarily transferable to other metal-ligand combinations. The ability of density functional approaches to model remote substituent effects is explored. Using the iron(II) R,R’pytacn complexes2 as a case study it is shown that whilst density functional approaches predict the correct trend for these substituted pyridine complexes there are occasional outliers. Traditional quantum approaches to the study of SCO, whilst accurate, are too time-consuming for the discovery of new complexes. Several LFMM parameter sets are optimised within this work. It is shown that this approach can accurately reproduce spin state energetics and geometries of iron(II) and cobalt(II) amines. A mixed donor type iron(II) amine/pyridine force field is also proposed. Through the utilisation of the drug discovery tools of the Molecular Operating Environment high throughput screening of cobalt(II) tetramine complexes is carried out. It is shown that ligands derived from macrocyclic rings display the most promise. These complexes, which are predicted to adopt a sawhorse geometry, show promise as SCO candidates are proposed as potential synthetic targets. This work illustrates the many exciting possibilities LFMM provides in the field transition metal computational chemistry allowing for theory to lead experiment rather than follo

178th University of Notre Dame Commencement

The Commencement Program includes: SCHEDULE OF EVENTS 3 GRADUATE SCHOOL COMMENCEMENT CEREMONY 8 DOCTORAL DEGREES 10 MASTER DEGREES 23 MENDOZA COLLEGE OF BUSINESS GRADUATE BUSINESS 38 GRADUATE ARCHITECTURE 44 LAW SCHOOL 45 UNIVERSITY COMMENCEMENT CEREMONY 49 COLLEGE OF ARTS AND LETTERS 52 COLLEGE OF SCIENCE 61 COLLEGE OF ENGINEERING 66 MENDOZA COLLEGE OF BUSINESS 71 SCHOOL OF ARCHITECTURE 77 VALEDICTORIAN CANDIDATES 78 EMERITI FACULTY 78 HONOR SOCIETIES 78 AWARDS AND PRIZES 85 TASSELS 10