Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity. FCs are commonly represented as correlation matrices that are symmetric positive definite (SPD) lying on or inside the SPD manifold. Since the geometry on the SPD manifold is non-Euclidean, the inter-related entries of FCs undermine the use of Euclidean-based distances. By projecting FCs into a tangent space, we can obtain tangent functional connectomes (tangent-FCs). Tangent-FCs have shown a higher predictive power of behavior and cognition, but no studies have evaluated the effect of such projections with respect to fingerprinting. We hypothesize that tangent-FCs have a higher fingerprint than regular FCs. Fingerprinting was measured by identification rates (ID rates) on test-retest FCs as well as on monozygotic and dizygotic twins. Our results showed that identification rates are systematically higher when using tangent-FCs. Specifically, we found: (i) Riemann and log-Euclidean matrix references systematically led to higher ID rates. (ii) In tangent-FCs, Main-diagonal regularization prior to tangent space projection was critical for ID rate when using Euclidean distance, whereas barely affected ID rates when using correlation distance. (iii) ID rates were dependent on condition and fMRI scan length. (iv) Parcellation granularity was key for ID rates in FCs, as well as in tangent-FCs with fixed regularization, whereas optimal regularization of tangent-FCs mostly removed this effect. (v) Correlation distance in tangent-FCs outperformed any other configuration of distance on FCs or on tangent-FCs across the fingerprint gradient (here sampled by assessing test-retest, Monozygotic and Dizygotic twins). (vi)ID rates tended to be higher in task scans compared to resting-state scans when accounting for fMRI scan length.Comment: 29 pages, 10 figures, 2 table

Advancing non-linear methods for coupled data assimilation across the atmosphere-land interface

In this thesis, I present two complementary frameworks to improve data assimila- tion in Earth system models, using the atmosphere-land interface as an exemplary case. As processes and components in the Earth system are coupled via interfaces, we would expect that assimilating observations from one Earth system component into another would improve the initialization of both components. In contrast to this expectation, it is often found that assimilation of atmospheric boundary layer observations into the land surface does not improve the analysis of the latter component. To disentangle the effects on the cross-compartmental assimilation, I take a step back from operational methods and use the coupled atmosphere-land modelling platform TerrSysMP in idealized twin experiments. I synthesize hourly and sparsely-distributed 2-metre-temperature observations from a single "nature" run. I subsequently assimilate these observations into the soil moisture with dif- ferent types of data assimilation methods. Based on this experimental structure, I test advanced data assimilation methods without model errors or biases. As my first framework, I propose to use localized ensemble Kalman filters for the unification of coupled data assimilation in Earth system models. To validate this framework, I conduct comparison experiments with a localized ensemble transform Kalman filter and a simplified extended Kalman filter, as similarly used at the ECMWF. Based on my developed environment, I find that we can assimilate 2-metre-temperature observations to improve the soil moisture analysis. In addition, hourly-updating the soil moisture with an ensemble Kalman filter decreases the error within the soil moisture analysis by up to 50 % compared to a daily-smoothing with a simplified extended Kalman filter. As a consequence, observations from the atmospheric boundary layer can be directly assimilated into the land surface model without a need of any intermediate interpolation, as normally used in land surface data assimilation. The improvement suggests that the land surface can be updated based on the same hourly cycle as used for mesoscale data assimilation. My results therefore prove that a unification of methods for data assimilation across the atmosphere-land interface is possible. As my second framework, I propose to use feature-based data assimilation to stabilize cross-compartmental data assimilation. To validate this framework, I use my implementation of an ensemble Kalman smoother that applies its analysis at the beginning of an assimilation window and resembles 4DEnVar. This smoother takes advantage of temporal dependencies in the atmosphere-land interface and improves the soil moisture analysis compared to the ensemble Kalman filter by 10 %. Subsequently based on this smoother, I introduce fingerprint operators as observational feature extractor into cross-compartmental data assimilation. These fingerprint operators take advantage of characteristic fingerprints in the difference between observations and model that point towards forecast errors, possibly in another Earth system component. As main finding, this concept can condense the information from the diurnal cycle in 2-metre-temperature observations into two observational features. This condensation makes the soil moisture analysis more robust against a miss-specified localization radius and errors in the observational covariance. Finally, I provide two new theoretical approaches to automatically learn such observational features with machine learning. In the first approach, I generalize ensemble Kalman filter with observational features to a novel kernelized ensemble transform Kalman filter.automatically This kernelized filter automatically con- structs the feature extractor on the basis of the given ensemble data and a chosen kernel function. In the second approach, I show that parameters within the data assimilation can be learned by variational Bayes. In this way, we can find whole distributions for parameters in data assimilation and, thus, determining their un- certainties. Furthermore, I prove the ensemble transform Kalman filter as a special solution of variational Bayes in the linearized-Gaussian case. These results suggest a possibility to specify the feature extractor as neural network and to train it with variational Bayes. These two approaches therefore prove that developments in machine learning can be used to extend data assimilation.In dieser Arbeit stelle ich zwei unterschiedliche Frameworks vor, um die Ini- tialisierung in gekoppelten Erdsystemmodellen für die Wettervorhersage zu verbessern. Dabei benutze ich die Schnittstelle zwischen der Atmosphäre und der Landoberfläche als Beispiel. Diese Schnittstelle bietet mir die Möglichkeit zu unter- suchen, in wie weit gekoppelte Datenassimilierung möglich ist. Prozesse und Kom- ponenten des Klimasystems sind über verschiedene Schnittstellen miteinander verbunden. Von daher würden wir erwarten, dass Beobachtungen aus der atmo- sphärischen Grenzschicht, auch die Initialisierung von Bodenmodellen verbessern, allerdings wurde in verschiedenen vorangegangenden Studien gezeigt, dass dies nicht der Fall ist. Um die Einflüsse von unterschiedlichen Fehler-Faktoren auf die Datenassimilierung zu reduzieren, benutze ich Experimente, die im Vergleich zur operationellen Wettervorhersage vereinfacht sind. Hierfür benutze ich das gekop- pelte Atmosphären-Land Vorhersagemodel TerrSysMP. All diese Experimente basieren auf einem Lauf ohne Datenassimilierung, den ich als meine "Natur" definiere. Aus diesem Naturlauf extrahiere ich künstliche 2-Meter-Temperatur Beobachtungen, welche dann mit unterschiedlichen Datenassimilierungsverfahren in die Bodenfeuchte assimiliert werden. Mit dieser Art von Experimenten teste ich fortschrittliche und nicht-lineare Datenassimilierungsverfahren für die Atmosphären- Land-Schnittstelle. Als erstes Framework schlage ich vor, einen lokalisierten Ensemble-Kalman-Filter für eine vereinheitlichte Datenassimilierung in Erdsystemmodellen zu verwenden. Um dieses Framework zu validieren, mache ich Vergleichsexperimente mit dem eben erwähnten lokalisierten Ensemble-Kalman-Filter und einem vereinfachten Extended-Kalman-Filter, der in ähnlicher Form beim Europäischen Zentrum für mittelfristige Wettervorhersage verwendet wird. Basierend auf meiner entwick- elten Umgebung zeige ich, dass 2-Meter-Temperatur Beobachtungen dafür ver- wendet werden können, um die Initialisierung der Bodenfeuchte zu verbessern. Der lokalisierte Ensemble-Kalman Filter reduziert zusätzlich den Fehler in der Ini- tialisierung der Bodenfeuchte um bis zu 50 %, im Vergleich zu dem vereinfachten Extended-Kalman-Filter. Dies zeigt zum ersten Mal, dass Beobachtungen aus der atmosphärischen Grenzschicht, direkt für die Initialisierung der Bodenfeuchte, ver- wendet werden können, ohne den Umweg einer Interpolierung zu nehmen, wie es bei dem vereinfachten Extended-Kalman-Filter der Fall ist. Darüberhinausge- hend legen diese Verbesserungen nahe, dass die Landoberfläche mit der gleichen stündlichen Aktualisierungs-Rate, wie die Atmosphäre, initialisiert werden kann. Deshalb beweisen diese Ergebnisse, dass eine vereinheitlichte Datenassimilierung über die Atmosphären-Land-Schnittstelle hinweg möglich ist. Als zweites Framework schlage ich vor, anstatt von Beobachtungen, Merkmale dieser Beobachtung zu assimilieren. Dies kann die Assimilierung, über die Atmosphären-Land Schnittstelle hinweg, verbessern. Um dieses Framework zu validieren, führe ich einen Ensemble-Kalman-Smoother ein. Dieser Ensemble- Kalman-Smoother initialisiert die Bodenfeuchte auf Basis eines Assimilierungs- fensters, ähnlich dem variationsgetriebenem vierdimensionellem Verfahren. Mit diesem Ensemble-Kalman-Smoother zeige ich, dass es möglich ist, zeitliche Ab- hängigkeiten innerhalb der Atmospähren-Land-Schnittstelle in der Datenassimi- lierung zu verwenden. Die Verwendung dieser Abhängigkeiten verbessert hierbei die Initialisierung der Bodenfeuchte. Auf Basis dieser Methodik, führe ich Oper- atoren ein, die Fingerabdrücke innerhalb von Beobachtungen ausnutzen. Diese Fingerabdruck-Operatoren nutze ich dafür, um Vorhersage-Fehler in anderen Komponenten des Erdsystems zu finden. Für die 2-Meter-Temperatur zeige ich, dass Informationen aus dem Tagesverlauf der Temperatur in 2 unterschiedliche Merkmale kondensiert werden können. Diese Kondensation macht die Initial- isierung der Bodenfeuchte robuster gegen Störungen innerhalb der Lokalisierung und der Beobachtungskovarianzen. Deshalb beweisen diese Ergebnisse, dass die eingeführten Fingerabdruck-Operatoren, die Datenassimilierung über die Atmosphären-Land Schnittstelle hinweg stabilisieren. Als letzten Punkte führe ich zwei neue, theoretische, Ansätze ein, um solche Beobachtungsmerkmale automatisch mit maschinellem Lernen zu finden. In meinem ersten Ansatz zeige ich, dass der merkmal-basierte Ensemble-Kalman- Filter unter dem Deckmantel des kernbasierten Ensemble-Transform-Kalman- Filter generalisiert werden kann. Hierbei lernt die Datenassimilierung automa- tisch die wichtigsten Beobachtungsmerkmale auf Basis der Ensemble Daten und einem gewählten Kern. In meinem zweiten Ansatz, zeige ich, dass Parameter des Ensemble-Kalman Filters mit variationsgetriebenen Bayesianischen Meth- oden erlernt werden können. Mit dieser Bayesianischen Methode kann die gesamte Wahrscheinlichkeitsverteilung der Parameter herausgefunden und so Unsicherheiten, innerhalb dieser, dargestellt werden können. Zusätzlich beweise ich, dass der Ensemble-Kalman-Filters eine spezielle Lösung dieses Ansatze im linear-Gaussischen Fall ist. Als Konsequenz, deute ich an, dass wir die Beobach- tungsmerkmale durch neuronale Netzwerke ersetzen können, die mit Hilfe dieses Ansatze erlernt werden. Von daher beweisen diese beiden Ansätze, dass Entwick- lungen im maschinellen Lernen dafür genutzt werden können, um Datenassimi- lierungsmethoden zu erweitern und möglicherweise zu verbessern

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

Machine Learning Applications for Drug Repurposing

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected until a very late stage of the drug discovery, where the discovery of drug-induced side effects and potential drug resistance can decrease the value of the drug and even completely invalidate the use of the drug. Thus, a new paradigm in drug discovery is needed. Structural systems pharmacology is a new paradigm in drug discovery that the drug activities are studied by data-driven large-scale models with considerations of the structures and drugs. Structural systems pharmacology will model, on a genome scale, the energetic and dynamic modifications of protein targets by drug molecules as well as the subsequent collective effects of drug-target interactions on the phenotypic drug responses. To date, however, few experimental and computational methods can determine genome-wide protein-ligand interaction networks and the clinical outcomes mediated by them. As a result, the majority of proteins have not been charted for their small molecular ligands; we have a limited understanding of drug actions. To address the challenge, this dissertation seeks to develop and experimentally validate innovative computational methods to infer genome-wide protein-ligand interactions and multi-scale drug-phenotype associations, including drug-induced side effects. The hypothesis is that the integration of data-driven bioinformatics tools with structure-and-mechanism-based molecular modeling methods will lead to an optimal tool for accurately predicting drug actions and drug associated phenotypic responses, such as side effects. This dissertation starts by reviewing the current status of computational drug discovery for complex diseases in Chapter 1. In Chapter 2, we present REMAP, a one-class collaborative filtering method to predict off-target interactions from protein-ligand interaction network. In our later work, REMAP was integrated with structural genomics and statistical machine learning methods to design a dual-indication polypharmacological anticancer therapy. In Chapter 3, we extend REMAP, the core method in Chapter 2, into a multi-ranked collaborative filtering algorithm, WINTF, and present relevant mathematical justifications. Chapter 4 is an application of WINTF to repurpose an FDA-approved drug diazoxide as a potential treatment for triple negative breast cancer, a deadly subtype of breast cancer. In Chapter 5, we present a multilayer extension of REMAP, applied to predict drug-induced side effects and the associated biological pathways. In Chapter 6, we close this dissertation by presenting a deep learning application to learn biochemical features from protein sequence representation using a natural language processing method

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

IDENTITY CRISIS: WHEN FACE RECOGNITION MEETS TWINS AND PRIVACY

Ph.DDOCTOR OF PHILOSOPH

Learning the Structure of High-Dimensional Manifolds with Self-Organizing Maps for Accurate Information Extraction

This paper was submitted by the author prior to final official version. For official version please see http://hdl.handle.net/1911/70515This work aims to improve the capability of accurate information extraction from high-dimensional data, with a specific neural learning paradigm, the Self-Organizing Map (SOM). The SOM is an unsupervised learning algorithm that can faithfully sense the manifold structure and support supervised learning of relevant information from the data. Yet open problems regarding SOM learning exist. We focus on the following two issues. 1. Evaluation of topology preservation. Topology preservation is essential for SOMs in faithful representation of manifold structure. However, in reality, topology violations are not unusual, especially when the data have complicated structure. Measures capable of accurately quantifying and informatively expressing topology violations are lacking. One contribution of this work is a new measure, the Weighted Differential Topographic Function (WDTF), which differentiates an existing measure, the Topographic Function (TF), and incorporates detailed data distribution as an importance weighting of violations to distinguish severe violations from insignificant ones. Another contribution is an interactive visual tool, TopoView, which facilitates the visual inspection of violations on the SOM lattice. We show the effectiveness of the combined use of the WDTF and TopoView through a simple two-dimensional data set and two hyperspectral images. 2. Learning multiple latent variables from high-dimensional data. We use an existing two-layer SOM-hybrid supervised architecture, which captures the manifold structure in its SOM hidden layer, and then, uses its output layer to perform the supervised learning of latent variables. In the customary way, the output layer only uses the strongest output of the SOM neurons. This severely limits the learning capability. We allow multiple, k, strongest responses of the SOM neurons for the supervised learning. Moreover, the fact that different latent variables can be best learned with different values of k motivates a new neural architecture, the Conjoined Twins, which extends the existing architecture with additional copies of the output layer, for preferential use of different values of k in the learning of different latent variables. We also automate the customization of k for different variables with the statistics derived from the SOM. The Conjoined Twins shows its effectiveness in the inference of two physical parameters from Near-Infrared spectra of planetary ices