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

The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics

We describe the ‘Structure-Activity Relationship (SAR) Matrix’ (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research

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

Activity Cliff Prediction: Dataset and Benchmark

Activity cliffs (ACs), which are generally defined as pairs of structurally similar molecules that are active against the same bio-target but significantly different in the binding potency, are of great importance to drug discovery. Up to date, the AC prediction problem, i.e., to predict whether a pair of molecules exhibit the AC relationship, has not yet been fully explored. In this paper, we first introduce ACNet, a large-scale dataset for AC prediction. ACNet curates over 400K Matched Molecular Pairs (MMPs) against 190 targets, including over 20K MMP-cliffs and 380K non-AC MMPs, and provides five subsets for model development and evaluation. Then, we propose a baseline framework to benchmark the predictive performance of molecular representations encoded by deep neural networks for AC prediction, and 16 models are evaluated in experiments. Our experimental results show that deep learning models can achieve good performance when the models are trained on tasks with adequate amount of data, while the imbalanced, low-data and out-of-distribution features of the ACNet dataset still make it challenging for deep neural networks to cope with. In addition, the traditional ECFP method shows a natural advantage on MMP-cliff prediction, and outperforms other deep learning models on most of the data subsets. To the best of our knowledge, our work constructs the first large-scale dataset for AC prediction, which may stimulate the study of AC prediction models and prompt further breakthroughs in AI-aided drug discovery. The codes and dataset can be accessed by https://drugai.github.io/ACNet/

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

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

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

Reduced collision fingerprints and pairwise molecular comparisons for explainable property prediction using Deep Learning

Les relations entre la structure des composés chimiques et leurs propriétés sont complexes et à haute dimension. Dans le processus de développement de médicaments, plusieurs proprié- tés d’un composé doivent souvent être optimisées simultanément, ce qui complique encore la tâche. Ce travail explore deux représentations des composés chimiques pour les tâches de prédiction des propriétés. L’objectif de ces représentations proposées est d’améliorer l’explicabilité afin de faciliter le processus d’optimisation des propriétés des composés. Pre- mièrement, nous décomposons l’algorithme ECFP (Extended connectivity Fingerprint) et le rendons plus simple pour la compréhension humaine. Nous remplaçons une fonction de hachage sujet aux collisions par une relation univoque de sous structure à bit. Nous consta- tons que ce changement ne se traduit pas par une meilleure performance prédictive d’un perceptron multicouche par rapport à l’ECFP. Toutefois, si la capacité du prédicteur est ra- menée à celle d’un prédicteur linéaire, ses performances sont meilleures que celles de l’ECFP. Deuxièmement, nous appliquons l’apprentissage automatique à l’analyse des paires molécu- laires appariées (MMPA), un paradigme de conception du développement de médicaments. La MMPA compare des paires de composés très similaires, dont la structure diffère par une modification sur un site. Nous formons des modèles de prédiction sur des paires de com- posés afin de prédire les différences d’activité. Nous utilisons des contraintes de similarité par paires comme MMPA, mais nous utilisons également des paires échantillonnées de façon aléatoire pour entraîner les modèles. Nous constatons que les modèles sont plus performants sur des paires choisies au hasard que sur des paires avec des contraintes de similarité strictes. Cependant, les meilleurs modèles par paires ne sont pas capables de battre les performances de prédiction du modèle simple de base. Ces deux études, RCFP et comparaisons par paires, visent à aborder la prédiction des propriétés d’une manière plus compréhensible. En utili- sant l’intuition et l’expérience des chimistes médicinaux dans le cadre de la modélisation prédictive, nous espérons encourager l’explicabilité en tant que composante nécessaire des modèles cheminformatiques prédictifs.The relationships between the structure of chemical compounds and their properties are complex and high dimensional. In the drug development process, multiple properties of a compound often need to be optimized simultaneously, further complicating the task. This work explores two representations of chemical compounds for property prediction tasks. The goal of these suggested representations is improved explainability to better understand the compound property optimization process. First, we decompose the Extended Connectivity Fingerprint (ECFP) algorithm and make it more straightforward for human understanding. We replace a collision-prone hash function with a one-to-one substructure-to-bit relationship. We find that this change which does not translate to higher predictive performance of a multi- layer perceptron compared to ECFP. However, if the capacity of the predictor is lowered to that of a linear predictor, it does perform better than ECFP. Second, we apply machine learning to Matched Molecular Pair Analysis (MMPA), a drug development design paradigm. MMPA compares pairs of highly similar compounds, differing in structure by modification at one site. We train prediction models on pairs of compounds to predict differences in activity. We use pairwise similarity constraints like MMPA, but also use randomly sampled pairs to train the models. We find that models perform better on randomly chosen pairs than on pairs with strict similarity constraints. However, the best pairwise models are not able to beat the prediction performance of the simpler baseline single model. Both of these investigations, RCFP and pairwise comparisons, aim to approach property prediction in a more explainable way. By using intuition and experience of medicinal chemists within predictive modelling, we hope to encourage explainability as a necessary component of predictive cheminformatic models