An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

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

Entwicklung einer computergestützten Methode zum reaktionsbasierten De-Novo-Design wirkstoffartiger Verbindungen

A new method for computer-based de novo design of drug candidate structures is proposed. DOGS (Design of Genuine Structures) features a ligand-based strategy to suggest new molecular structures. The quality of designed compounds is assessed by a graph kernel method measuring the distance of designed molecules to a known reference ligand. Two graph representations of molecules (molecular graph and reduced graph) are implemented to feature different levels of abstraction from the molecular structure. A fully deterministic construction procedure explicitly designed to facilitate synthesizability of proposed structures is realized: DOGS uses readily available synthesis building blocks and established reaction schemes to assemble new molecules. This approach enables the software to propose not only the final compounds, but also to give suggestions for synthesis routes to generate them at the bench. The set of synthesis schemes comprises about 83 chemical reactions. Special focus was put on ring closure reactions forming drug-like substructures. The library of building blocks consists of about 25,000 readily available synthesis building blocks. DOGS builds up new structures in a stepwise process. Each virtual synthesis step adds a fragment to the growing molecule until a stop criterion (upper threshold for molecular mass or number of synthesis steps) is fulfilled. In a theoretical evaluation, a set of ~1,800 molecules proposed by DOGS is analyzed for critical properties of de novo designed compounds. The software is able to suggest drug-like molecules (79% violate less than two of Lipinski’s ‘rule of five’). In addition, a trained classifier for drug-likeness assigns a score >0.8 to 51% of the designed molecules (with 1.0 being the top score). In addition, most of the DOGS molecules are deemed to be synthesizable by a retro-synthesis descriptor (77% of molecules score in the top 10% of the decriptor’s value range). Calculated logP(o/w) values of constructed molecules resemble a unimodal distribution centred close to the mean of logP(o/w) values calculated for the reference compounds. A structural analysis of selected designs reveals that DOGS is capable of constructing molecules reflecting the overall topological arrangement of pharmacophoric features found in the reference ligands. At the same time, the DOGS designs represent innovative compounds being structurally distinct from the references. Synthesis routes for these examples are short and seem feasible in most cases. Some reaction steps might need modification by using protecting groups to avoid unwanted side reactions. Plausible bioisosters for known privileged fragments addressing the S1 pocket of trypsin were proposed by DOGS in a case study. Three of them can be found in known trypsin inhibitors as S1-adressing side chains. The software was also tested in two prospective case studies to design bioactive compounds. DOGS was applied to design ligands for human gamma-secretase and human histamine receptor subtype 4 (hH4R). Two selected designs for gamma-secretase were readily synthesizable as suggested by the software in one-step reactions. Both compounds represent inverse modulators of the target molecule. In a second case study, a ligand candidate selected for hH4R was synthesized exactly following the three-step synthesis plan suggested by DOGS. This compound showed low activity on the target structure. The concept of DOGS is able to deliver synthesizable and bioactive compounds. Suggested synthesis plans of selected compounds were readily pursuable. DOGS can therefore serve as a valuable idea generator for the design of new pharmacological active compounds.Im Rahmen der vorliegenden Arbeit wird eine neue Methode zum computergestützten de novo Design von wirkstoffartigen Molekülen vorgestellt. Ziel ist es, automatisiert und zielgerichtet neuartige Moleküle mit biologischer Aktivität zu entwerfen. Das entwickelte Programm DOGS (Design of Genuine Structures) schlägt zusätzlich zu den chemischen Verbindungen mögliche Strategien zu deren Synthese vor. Ein vollständig deterministischer Konstruktionsalgorithmus verwendet verfügbare Synthesebausteine und etablierte chemische Reaktionen zum Aufbau der neuen Moleküle. Die Bibliothek der Synthesebausteine umfasst etwa 25.000 Moleküle mit einer molekularen Masse zwischen 30 und 300 Da. Die Sammlung der Reaktionen zur Verknüpfung der Bausteine besteht aus 83 literaturbeschriebenen chemischen Reaktionen. Ein Großteil stellt Syntheseschritte zur Generierung neuer Ringsysteme dar. DOGS baut neue Moleküle schrittweise auf: In jedem virtuellen Syntheseschritt wird ein neues Fragment an das wachsende Molekül angefügt, bis eines der Stoppkriterien (Überschreitung einer maximalen molekulare Masse oder Anzahl Syntheseschritte) erfüllt ist. Zur Bewertung der Qualität der Zischen- und Endprodukte wird eine ligandenbasierte Strategie verwendet. Die entstehenden Moleküle werden mit einem bekannten Referenzliganden verglichen, welcher die gewünschte biologische Aktivität aufweist. Das Verfahren zielt dabei auf die Maximierung der Ähnlichkeit der neu konstruierten Moleküle zur Referenz ab. Eine Graphkernmethode berechnet die Ähnlichkeit zum Referenzliganden anhand des Vergleichs ihrer zweidimensionalen molekularen Struktur. In einer theoretischen Auswertung des Programms werden ca. 1.800 generierte potentielle Trypsin-Inhibitoren hinsichtlich solcher Eigenschaften analysiert, welche für neu entworfene Verbindungen kritisch sind: DOGS ist in der Lage wirkstoffartige Moleküle zu entwerfen (79% verletzen weniger als zwei von Lipinskis 'rule of five' Kriterien zur Abschätzung der oralen Bioverfügbarkeit). Zusätzlich wurde die Wirkstoffartigkeit der DOGS-Moleküle durch einen trainierten Klassifizieralgorithmus bewertet. Hierbei erhielten 51% der Verbindungen einen Wert in den oberen 20% des Wertebereichs des Klassifizierers. Weiterhin wird die synthetische Zugänglichkeit für den Großteil der computergenerierten Moleküle als hoch eingeschätzt (77% erhalten einen Wert in den oberen 10% des Wertebereichs eines Deskriptors zur Abschätzung der Synthetisierbarkeit). Die berechneten logP(o/w) Werte der konstruierten Moleküle entsprechen in ihrer Verteilung denen der Referenzliganden. Die Untersuchung der vorgeschlagenen Trypsin-Inhibitoren auf Bioisostere zur Adressierung der S1-Bindetasche zeigt, dass hierfür plausible Vorschläge von DOGS generiert werden. Der Großteil ist potentiell in der Lage eine kritische ladungsvermittelte Interaktion mit dem Protein in der S1-Bindetasche einzugehen. Unter den Vorschlägen befinden sich unter anderem auch drei Seitenketten, für die Interaktionen mit der S1-Bindetasche von Trypsin experimentell bestätigt sind. Eine Analyse ausgewählter Beispiele aus verschiedenen Läufen zum Ligandenentwurf für unterschiedliche biologische Zielmoleküle zeigt, dass das Programm in der Lage ist, die generelle topologische Anordnung potentieller Interaktionspunkte der Referenzliganden in den neu erzeugten Molekülen beizubehalten. Gleichzeitig sind diese Moleküle strukturell verschieden im Vergleich zu den Referenzliganden. Die generierten Synthesewege sind kurz und erscheinen in den meisten Fällen plausibel. Für einige der Syntheseschritte wird bei der praktischen Umsetzung der ergänzende Einsatz von Schutzgruppen notwendig sein, um unerwünschte Nebenreaktionen zu vermeiden. Die Software wurde zusätzlich zu den theoretischen Analysen in prospektiven Studien zum Ligandenentwurf praktisch evaluiert. Hierzu wurde DOGS zur Generierung von Liganden des humanen Histaminrezeptors 4 (hH4R) sowie der humanen gamma-Sekretase eingesetzt. Für hH4R wurde einer der entworfenen potentiellen Liganden synthetisiert, wobei der vorgeschlagene Syntheseweg exakt nachvollzogen werden konnte. Der Ligand weist eine geringfügige Affinität zum Histaminrezeptor auf. Für die gamma-Sekretase wurden zwei der entworfenen Moleküle zur Synthese und Testung ausgewählt. In beiden Fällen konnte auch hier die von DOGS vorgeschlagene Synthesestrategie nachvollzogen werden. Anschließende in vitro Analysen wiesen beide Verbindungen als inverse Modulatoren der gamma-Sekretase aus. Das Konstruktionskonzept von DOGS ist in der Lage, bioaktive Substanzen vorzuschlagen. Diese sind synthetisch zugänglich und können nach der vorgeschlagenen Strategie synthetisiert werden. Somit kann das Programm als Ideengenerator für den Entwurf neuer bioaktiver Moleküle dienen

Metabolomics : a tool for studying plant biology

In recent years new technologies have allowed gene expression, protein and metabolite profiles in different tissues and developmental stages to be monitored. This is an emerging field in plant science and is applied to diverse plant systems in order to elucidate the regulation of growth and development. The goal in plant metabolomics is to analyze, identify and quantify all low molecular weight molecules of plant organisms. The plant metabolites are extracted and analyzed using various sensitive analytical techniques, usually mass spectrometry (MS) in combination with chromatography. In order to compare the metabolome of different plants in a high through-put manner, a number of biological, analytical and data processing steps have to be performed. In the work underlying this thesis we developed a fast and robust method for routine analysis of plant metabolite patterns using Gas Chromatography-Mass Spectrometry (GC/MS). The method was performed according to Design of Experiment (DOE) to investigate factors affecting the extraction and derivatization of the metabolites from leaves of the plant Arabidopsis thaliana. The outcome of metabolic analysis by GC/MS is a complex mixture of approximately 400 overlapping peaks. Resolving (deconvoluting) overlapping peaks is time-consuming, difficult to automate and additional processing is needed in order to compare samples. To avoid deconvolution being a major bottleneck in high through-put analyses we developed a new semi-automated strategy using hierarchical methods for processing GC/MS data that can be applied to all samples simultaneously. The two methods include base-line correction of the non-processed MS-data files, alignment, time-window determinations, Alternating Regression and multivariate analysis in order to detect metabolites that differ in relative concentrations between samples. The developed methodology was applied to study the effects of the plant hormone GA on the metabolome, with specific emphasis on auxin levels in Arabidopsis thaliana mutants defective in GA biosynthesis and signalling. A large series of plant samples was analysed and the resulting data were processed in less than one week with minimal labour; similar to the time required for the GC/MS analyses of the samples

Rethinking drug design in the artificial intelligence era

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them

Methods for the Analysis of Matched Molecular Pairs and Chemical Space Representations

Compound optimization is a complex process where different properties are optimized to increase the biological activity and therapeutic effects of a molecule. Frequently, the structure of molecules is modified in order to improve their property values. Therefore, computational analysis of the effects of structure modifications on property values is of great importance for the drug discovery process. It is also essential to analyze chemical space, i.e., the set of all chemically feasible molecules, in order to find subsets of molecules that display favorable property values. This thesis aims to expand the computational repertoire to analyze the effect of structure alterations and visualize chemical space. Matched molecular pairs are defined as pairs of compounds that share a large common substructure and only differ by a small chemical transformation. They have been frequently used to study property changes caused by structure modifications. These analyses are expanded in this thesis by studying the effect of chemical transformations on the ionization state and ligand efficiency, both measures of great importance in drug design. Additionally, novel matched molecular pairs based on retrosynthetic rules are developed to increase their utility for prospective use of chemical transformations in compound optimization. Further, new methods based on matched molecular pairs are described to obtain preliminary SAR information of screening hit compounds and predict the potency change caused by a chemical transformation. Visualizations of chemical space are introduced to aid compound optimization efforts. First, principal component plots are used to rationalize a matched molecular pair based multi-objective compound optimization procedure. Then, star coordinate and parallel coordinate plots are introduced to analyze drug-like subspaces, where compounds with favorable property values can be found. Finally, a novel network-based visualization of high-dimensional property space is developed. Concluding, the applications developed in this thesis expand the methodological spectrum of computer-aided compound optimization

Industrial Waste

This book is intended to fulfil the need for state-of-the-art development on the industrial wastes from different types of industries. Most of the chapters are based upon the ongoing research, how the different types of wastes are most efficiently treated and minimized, technologies of wastes control and abatement, and how they are released to the environment and their associated impact. A few chapters provide updated review summarizing the status and prospects of industrial waste problems from different perspectives. The book is comprehensive and not limited to a partial discussion of industrial waste, so the readers are acquainted with the latest information and development in the area, where different aspects are considered. The user can find both introductory material and more specific material based on interests and problems. For additional questions or comments, the users are encouraged to contact the authors

Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe): A Computer-Aided Methodology for Multi-Criteria Drug Discovery

Doutoramento em Ciências FarmacêuticasPhD in Pharmaceutical Science