Computational Methods for Structure-Activity Relationship Analysis and Activity Prediction

Structure-activity relationship (SAR) analysis of small bioactive compounds is a key task in medicinal chemistry. Traditionally, SARs were established on a case-by-case basis. However, with the arrival of high-throughput screening (HTS) and synthesis techniques, a surge in the size and structural heterogeneity of compound data is seen and the use of computational methods to analyse SARs has become imperative and valuable. In recent years, graphical methods have gained prominence for analysing SARs. The choice of molecular representation and the method of assessing similarities affects the outcome of the SAR analysis. Thus, alternative methods providing distinct points of view of SARs are required. In this thesis, a novel graphical representation utilizing the canonical scaffold-skeleton definition to explore meaningful global and local SAR patterns in compound data is introduced. Furthermore, efforts have been made to go beyond descriptive SAR analysis offered by the graphical methods. SAR features inferred from descriptive methods are utilized for compound activity predictions. In this context, a data structure called SAR matrix (SARM), which is reminiscent of conventional R-group tables, is utilized. SARMs suggest many virtual compounds that represent as of yet unexplored chemical space. These virtual compounds are candidates for further exploration but are too many to prioritize simply on the basis of visual inspection. Conceptually different approaches to enable systematic compound prediction and prioritization are introduced. Much emphasis is put on evolving the predictive ability for prospective compound design. Going beyond SAR analysis, the SARM method has also been adapted to navigate multi-target spaces primarily for analysing compound promiscuity patterns. Thus, the original SARM methodology has been further developed for a variety of medicinal chemistry and chemogenomics applications

Computational Analysis of Structure-Activity Relationships : From Prediction to Visualization Methods

Understanding how structural modifications affect the biological activity of small molecules is one of the central themes in medicinal chemistry. By no means is structure-activity relationship (SAR) analysis a priori dependent on computational methods. However, as molecular data sets grow in size, we quickly approach our limits to access and compare structures and associated biological properties so that computational data processing and analysis often become essential. Here, different types of approaches of varying complexity for the analysis of SAR information are presented, which can be applied in the context of screening and chemical optimization projects. The first part of this thesis is dedicated to machine-learning strategies that aim at de novo ligand prediction and the preferential detection of potent hits in virtual screening. High emphasis is put on benchmarking of different strategies and a thorough evaluation of their utility in practical applications. However, an often claimed disadvantage of these prediction methods is their "black box" character because they do not necessarily reveal which structural features are associated with biological activity. Therefore, these methods are complemented by more descriptive SAR analysis approaches showing a higher degree of interpretability. Concepts from information theory are adapted to identify activity-relevant structure-derived descriptors. Furthermore, compound data mining methods exploring prespecified properties of available bioactive compounds on a large scale are designed to systematically relate molecular transformations to activity changes. Finally, these approaches are complemented by graphical methods that primarily help to access and visualize SAR data in congeneric series of compounds and allow the formulation of intuitive SAR rules applicable to the design of new compounds. The compendium of SAR analysis tools introduced in this thesis investigates SARs from different perspectives

Chemoinformatics-Driven Approaches for Kinase Drug Discovery

Given their importance for the majority of cell physiology processes, protein kinases are among the most extensively studied protein targets in drug discovery. Inappropriate regulation of their basal levels results in pathophysiological disorders. In this regard, small-molecule inhibitors of human kinome have been developed to treat these conditions effectively and improve the survival rates and life quality of patients. In recent years, kinase-related data has become increasingly available in the public domain. These large amounts of data provide a rich knowledge source for the computational studies of kinase drug discovery concepts. This thesis aims to systematically explore properties of kinase inhibitors on the basis of publicly available data. Hence, an established "selectivity versus promiscuity" conundrum of kinase inhibitors is evaluated, close structural analogs with diverging promiscuity levels are analyzed, and machine learning is employed to classify different kinase inhibitor binding modes. In the first study, kinase inhibitor selectivity trends are explored on the kinase pair level where kinase structural features and phylogenetic relationships are used to explain the obtained selectivity information. Next, selectivity of clinical kinase inhibitors is inspected on the basis of cell-based profiling campaign results to consolidate the previous findings. Further, clinical candidates are mapped to medicinal chemistry sources and promiscuity levels of different inhibitor subsets are estimated, including designated chemical probes. Additionally, chemical probe analysis is extended to expert-curated representatives to correlate the views established by scientific community and evaluate their potential for chemical biology applications. Then, large-scale promiscuity analysis of kinase inhibitor data combining several public repositories is performed to subsequently explore promiscuity cliffs (PCs) and PC pathways and study structure-promiscuity relationships. Furthermore, an automated extraction protocol prioritizing the most informative pathways is proposed with focus on those containing promiscuity hubs. In addition, the generated promiscuity data structures including cliffs, pathways, and hubs are discussed for their potential in experimental and computational follow-ups and subsequently made publicly available. Finally, machine learning methods are used to develop classification models of kinase inhibitors with distinct experimental binding modes and their potential for the development of novel therapeutics is assessed

Application and Development of Computational Methods for Ligand-Based Virtual Screening

The detection of novel active compounds that are able to modulate the biological function of a target is the primary goal of drug discovery. Different screening methods are available to identify hit compounds having the desired bioactivity in a large collection of molecules. As a computational method, virtual screening (VS) is used to search compound libraries in silico and identify those compounds that are likely to exhibit a specific activity. Ligand-based virtual screening (LBVS) is a subdiscipline that uses the information of one or more known active compounds in order to identify new hit compounds. Different LBVS methods exist, e.g. similarity searching and support vector machines (SVMs). In order to enable the application of these computational approaches, compounds have to be described numerically. Fingerprints derived from the two-dimensional compound structure, called 2D fingerprints, are among the most popular molecular descriptors available. This thesis covers the usage of 2D fingerprints in the context of LBVS. The first part focuses on a detailed analysis of 2D fingerprints. Their performance range against a wide range of pharmaceutical targets is globally estimated through fingerprint-based similarity searching. Additionally, mechanisms by which fingerprints are capable of detecting structurally diverse active compounds are identified. For this purpose, two different feature selection methods are applied to find those fingerprint features that are most relevant for the active compounds and distinguish them from other compounds. Then, 2D fingerprints are used in SVM calculations. The SVM methodology provides several opportunities to include additional information about the compounds in order to direct LBVS search calculations. In a first step, a variant of the SVM approach is applied to the multi-class prediction problem involving compounds that are active against several related targets. SVM linear combination is used to recover compounds with desired activity profiles and deprioritize compounds with other activities. Then, the SVM methodology is adopted for potency-directed VS. Compound potency is incorporated into the SVM approach through potencyoriented SVM linear combination and kernel function design to direct search calculations to the preferential detection of potent hit compounds. Next, SVM calculations are applied to address an intrinsic limitation of similarity-based methods, i.e., the presence of similar compounds having large differences in their potency. An especially designed SVM approach is introduced to predict compound pairs forming such activity cliffs. Finally, the impact of different training sets on the recall performance of SVM-based VS is analyzed and caveats are identified

Computational Methods Generating High-Resolution Views of Complex Structure-Activity Relationships

The analysis of structure-activity relationships (SARs) of small bioactive compounds is a central task in medicinal chemistry and pharmaceutical research. The study of SARs is in principle not limited to computational methods, however, as data sets rapidly grow in size, advanced computational approaches become indispensable for SAR analysis. Activity landscapes are one of the preferred and widely used computational models to study large-scale SARs. Activity cliffs are cardinal features of activity landscape representations and are thought to contain high SAR information content. This work addresses major challenges in systematic SAR exploration and specifically focuses on the design of novel activity landscape models and comprehensive activity cliff analysis. In the first part of the thesis, two conceptually different activity landscape representations are introduced for compounds active against multiple targets. These models are designed to provide an intuitive graphical access to compounds forming single and multi-target activity cliffs and displaying multi-target SAR characteristics. Further, a systematic analysis of the frequency and distribution of activity cliffs is carried out. In addition, a large-scale data mining effort is designed to quantify and analyze fingerprint-dependent changes in SAR information. The second part of this work is dedicated to the concept of activity cliffs and their utility in the practice of medicinal chemistry. Therefore, a computational approach is introduced to search for detectable SAR advantages associated with activity cliffs. In addition, the question is investigated to what extent activity cliffs might be utilized as starting points in practical compound optimization efforts. Finally, all activity cliff configurations formed by currently available bioactive compounds are thoroughly examined. These configurations are further classified and their frequency of occurrence and target distribution are determined. Furthermore, the activity cliff concept is extended to explore the relation between chemical structures and compound promiscuity. The notion of promiscuity cliffs is introduced to deduce structural modifications that might induce large-magnitude promiscuity effects

Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations

A core focus in medicinal chemistry is the interpretation of structure-activity relationships (SARs) of small molecules. SAR analysis is typically carried out on a case-by-case basis for compound sets that share activity against a given target. Although SAR investigations are not a priori dependent on computational approaches, limitations imposed by steady rise in activity information have necessitated the use of such methodologies. Moreover, understanding SARs in multi-target space is extremely difficult. Conceptually different computational approaches are reported in this thesis for graphical SAR analysis in single- as well as multi-target space. Activity landscape models are often used to describe the underlying SAR characteristics of compound sets. Theoretical activity landscapes that are reminiscent of topological maps intuitively represent distributions of pair-wise similarity and potency difference information as three-dimensional surfaces. These models provide easy access to identification of various SAR features. Therefore, such landscapes for actual data sets are generated and compared with graph-based representations. Existing graphical data structures are adapted to include mechanism of action information for receptor ligands to facilitate simultaneous SAR and mechanism-related analyses with the objective of identifying structural modifications responsible for switching molecular mechanisms of action. Typically, SAR analysis focuses on systematic pair-wise relationships of compound similarity and potency differences. Therefore, an approach is reported to calculate SAR feature probabilities on the basis of these pair-wise relationships for individual compounds in a ligand set. The consequent expansion of feature categories improves the analysis of local SAR environments. Graphical representations are designed to avoid a dependence on preconceived SAR models. Such representations are suitable for systematic large-scale SAR exploration. Methods for the navigation of SARs in multi-target space using simple and interpretable data structures are introduced. In summary, multi-faceted SAR analysis aided by computational means forms the primary objective of this dissertation

Virtual compound screening and SAR analysis: method development and practical applications in the design of new serine and cysteine protease inhibitors

Virtual screening is an important tool in drug discovery that uses different computational methods to screen chemical databases for the identification of possible drug candidates. Most virtual screening methodologies are knowledge driven where the availability of information on either the nature of the target binding pocket or the type of ligand that is expect to bind is essential. In this regard, the information contained in X-ray crystal structures of protein-ligand complexes provides a detailed insight into the interactions between the protein and the ligand and opens the opportunity for further understanding of drug action and structure activity relationships at molecular level. Protein-ligand interaction information can be utilized to introduce target-specific interaction-based constraints in the design of focused combinatorial libraries. It can also be directly transformed into structural interaction fingerprints and can be applied in virtual screening to analyze docking studies or filter compounds. However, the integration of protein-ligand interaction information into two-dimensional compound similarity searching is not fully explored. Therefore, novel methods are still required to efficiently utilize protein-ligand interaction information in two-dimensional ligand similarity searching. Furthermore, application of protein-ligand interaction information in the interpretation of SARs at the ligand level needs further exploration. Thus, utilization of three-dimensional protein ligand interaction information in virtual screening and SAR analysis was the major aim of this thesis. The thesis is presented in two major parts. In the first part, utilization of three-dimensional protein-ligand interaction information for the development of a new hybrid virtual screening method and analysis of the nature of SARs in analog series at molecular level is presented. The second part of the thesis is focused on the application of different virtual screening methods for the identification of new cysteine and membrane-bound serine proteases inhibitors. In addition, molecular modeling studies were also applied to analyze the binding mode of structurally complex cyclic peptide inhibitors

Computational Methods for the Integration of Biological Activity and Chemical Space

One general aim of medicinal chemistry is the understanding of structure-activity relationships of ligands that bind to biological targets. Advances in combinatorial chemistry and biological screening technologies allow the analysis of ligand-target relationships on a large-scale. However, in order to extract useful information from biological activity data, computational methods are needed that link activity of ligands to their chemical structure. In this thesis, it is investigated how fragment-type descriptors of molecular structure can be used in order to create a link between activity and chemical ligand space. First, an activity class-dependent hierarchical fragmentation scheme is introduced that generates fragmentation pathways that are aligned using established methodologies for multiple alignment of biological sequences. These alignments are then used to extract consensus fragment sequences that serve as a structural signature for individual biological activity classes. It is also investigated how defined, chemically intuitive molecular fragments can be organized based on their topological environment and co-occurrence in compounds active against closely related targets. Therefore, the Topological Fragment Index is introduced that quantifies the topological environment complexity of a fragment in a given molecule, and thus goes beyond fragment frequency analysis. Fragment dependencies have been established on the basis of common topological environments, which facilitates the identification of activity class-characteristic fragment dependency pathways that describe fragment relationships beyond structural resemblance. Because fragments are often dependent on each other in an activity class-specific manner, the importance of defined fragment combinations for similarity searching is further assessed. Therefore, Feature Co-occurrence Networks are introduced that allow the identification of feature cliques characteristic of individual activity classes. Three differently designed molecular fingerprints are compared for their ability to provide such cliques and a clique-based similarity searching strategy is established. For molecule- and activity class-centric fingerprint designs, feature combinations are shown to improve similarity search performance in comparison to standard methods. Moreover, it is demonstrated that individual features can form activity-class specific combinations. Extending the analysis of feature cliques characteristic of individual activity classes, the distribution of defined fragment combinations among several compound classes acting against closely related targets is assessed. Fragment Formal Concept Analysis is introduced for flexible mining of complex structure-activity relationships. It allows the interactive assembly of fragment queries that yield fragment combinations characteristic of defined activity and potency profiles. It is shown that pairs and triplets, rather than individual fragments distinguish between different activity profiles. A classifier is built based on these fragment signatures that distinguishes between ligands of closely related targets. Going beyond activity profiles, compound selectivity is also analyzed. Therefore, Molecular Formal Concept Analysis is introduced for the systematic mining of compound selectivity profiles on a whole-molecule basis. Using this approach, structurally diverse compounds are identified that share a selectivity profile with selected template compounds. Structure-selectivity relationships of obtained compound sets are further analyzed

Regulatory acceptance of read-across

n/

Computational studies of biomolecules

In modern drug discovery, lead discovery is a term used to describe the overall process from hit discovery to lead optimisation, with the goal being to identify drug candidates. This can be greatly facilitated by the use of computer-aided (or in silico) techniques, which can reduce experimentation costs along the drug discovery pipeline. The range of relevant techniques include: molecular modelling to obtain structural information, molecular dynamics (which will be covered in Chapter 2), activity or property prediction by means of quantitative structure activity/property models (QSAR/QSPR), where machine learning techniques are introduced (to be covered in Chapter 1) and quantum chemistry, used to explain chemical structure, properties and reactivity. This thesis is divided into five parts. Chapter 1 starts with an outline of the early stages of drug discovery; introducing the use of virtual screening for hit and lead identification. Such approaches may roughly be divided into structure-based (docking, by far the most often referred to) and ligand-based, leading to a set of promising compounds for further evaluation. Then, the use of machine learning techniques, the issue of which will be frequently encountered, followed by a brief review of the "no free lunch" theorem, that describes how no learning algorithm can perform optimally on all problems. This implies that validation of predictive accuracy in multiple models is required for optimal model selection. As the dimensionality of the feature space increases, the issue referred to as "the curse of dimensionality" becomes a challenge. In closing, the last sections focus on supervised classification Random Forests. Computer-based analyses are an integral part of drug discovery. Chapter 2 begins with discussions of molecular docking; including strategies incorporating protein flexibility at global and local levels, then a specific focus on an automated docking program – AutoDock, which uses a Lamarckian genetic algorithm and empirical binding free energy function. In the second part of the chapter, a brief introduction of molecular dynamics will be given. Chapter 3 describes how we constructed a dataset of known binding sites with co-crystallised ligands, used to extract features characterising the structural and chemical properties of the binding pocket. A machine learning algorithm was adopted to create a three-way predictive model, capable of assigning each case to one of the classes (regular, orthosteric and allosteric) for in silico selection of allosteric sites, and by a feature selection algorithm (Gini) to rationalize the selection of important descriptors, most influential in classifying the binding pockets. In Chapter 4, we made use of structure-based virtual screening, and we focused on docking a fluorescent sensor to a non-canonical DNA quadruplex structure. The preferred binding poses, binding site, and the interactions are scored, followed by application of an ONIOM model to re-score the binding poses of some DNA-ligand complexes, focusing on only the best pose (with the lowest binding energy) from AutoDock. The use of a pre-generated conformational ensemble using MD to account for the receptors' flexibility followed by docking methods are termed “relaxed complex” schemes. Chapter 5 concerns the BLUF domain photocycle. We will be focused on conformational preference of some critical residues in the flavin binding site after a charge redistribution has been introduced. This work provides another activation model to address controversial features of the BLUF domain