Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.

BACKGROUND: Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. RESULTS: Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. CONCLUSIONS: Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction.

The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like molecules. Broadening the boundaries of structure based drug design, these molecules are expected to share the same physiological target and have similar efficacy, as do known drug molecules sharing the same region in chemical property space. In the past few decades physicochemical and ADMET (absorption, distribution, metabolism, elimination, and toxicity) property predictors have been the subject of increased focus in academia and the pharmaceutical industry. Due to the ever increasing attention given to data mining and property predictions, we first discuss the sources of experimental pKa values and current methodologies used for pKa prediction in proteins and small molecules. Of particular concern is an analysis of the scope, statistical validity, overall accuracy, and predictive power of these methods. The expressed concerns are not limited to predicting pKa, but apply to all empirical predictive methodologies. In a bottom-up approach, we explored the influence of freely generated SMARTS string representations of molecular fragments on chelation and cytotoxicity. Later investigations, involving the derivation of predictive models, use stepwise regression to determine the optimal pool of SMARTS strings having the greatest influence over the property of interest. By applying a unique scoring system to sets of highly generalized SMARTS strings, we have constructed well balanced regression trees with predictive accuracy exceeding that of many published and commercially available models for cytotoxicity, pKa, and aqueous solubility. The methodology is robust, extremely adaptable, and can handle any molecular dataset with experimental data. This story details our struggles of data gathering, curation, and the development of a machine learning methodology able to derive and validate highly accurate regression trees capable of extremely fast property predictions. Regression trees created by our method are well suited to calculate descriptors for large in silico molecular libraries, facilitating data mining of chemical spaces in search of new lead molecules in drug discovery.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64627/1/adamclee_1.pd

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

Field-based Proteochemometric Models Derived from 3D Protein Structures : A Novel Approach to Visualize Affinity and Selectivity Features

Designing drugs that are selective is crucial in pharmaceutical research to avoid unwanted side effects. To decipher selectivity of drug targets, computational approaches that utilize the sequence and structural information of the protein binding pockets are frequently exploited. In addition to methods that rely only on protein information, quantitative approaches such as proteochemometrics (PCM) use the combination of protein and ligand descriptions to derive quantitative relationships with binding affinity. PCM aims to explain cross-interactions between the different proteins and ligands, hence facilitating our understanding of selectivity. The main goal of this dissertation is to develop and apply field-based PCM to improve the understanding of relevant molecular interactions through visual illustrations. Field-based description that depends on the 3D structural information of proteins enhances visual interpretability of PCM models relative to the frequently used sequence-based descriptors for proteins. In these field-based PCM studies, knowledge-based fields that explain polarity and lipophilicity of the binding pockets and WaterMap-derived fields that elucidate the positions and energetics of water molecules are used together with the various 2D / 3D ligand descriptors to investigate the selectivity profiles of kinases and serine proteases. Field-based PCM is first applied to protein kinases, for which designing selective inhibitors has always been a challenge, owing to their highly similar ATP binding pockets. Our studies show that the method could be successfully applied to pinpoint the regions influencing the binding affinity and selectivity of kinases. As an extension of the initial studies conducted on a set of 50 kinases and 80 inhibitors, field-based PCM was used to build classification models on a large dataset (95 kinases and 1572 inhibitors) to distinguish active from inactive ligands. The prediction of the bioactivities of external test set compounds or kinases with accuracies over 80% (Matthews correlation coefficient, MCC: ~0.50) and area under the ROC curve (AUC) above 0.8 together with the visual inspection of the regions promoting activity demonstrates the ability of field-based PCM to generate both predictive and visually interpretable models. Further, the application of this method to serine proteases provides an overview of the sub-pocket specificities, which is crucial for inhibitor design. Additionally, alignment-independent Zernike descriptors derived from fields were used in PCM models to study the influence of protein superimpositions on field comparisons and subsequent PCM modelling.Lääketutkimuksessa selektiivisten lääkeaineiden suunnittelu on ratkaisevan tärkeää haittavaikutusten välttämiseksi. Kohdeselektiivisyyden selvittämiseen käytetään usein tietokoneavusteisia menetelmiä, jotka hyödyntävät proteiinien sitoutumiskohtien sekvenssi- ja rakennetietoja. Proteiinilähtöisten menetelmien lisäksi kvantitatiiviset menetelmät kuten proteokemometria (proteochemometrics, PCM) yhdistävät sekä proteiinin että ligandin tietoja muodostaessaan kvantitatiivisen suhteen sitoutumisaffiniteettiin. PCM pyrkii selittämään eri proteiinien ja ligandien vuorovaikutuksia ja näin auttaa ymmärtämään selektiivisyyttä. Väitöstutkimuksen tavoitteena oli kehittää ja hyödyntää kenttäpohjaista proteokemometriaa, joka auttaa ymmärtämään relevantteja molekyylitasoisia vuorovaikutuksia visuaalisen esitystavan kautta. Proteiinin kolmiulotteisesta rakenteesta riippuva kenttäpohjainen kuvaus helpottaa PCM-mallien tulkintaa, etenkin usein käytettyihin sekvenssipohjaisiin kuvauksiin verrattuna. Näissä kenttäpohjaisissa PCM-mallinnuksissa käytettiin tietoperustaisia sitoutumistaskun polaarisuutta ja lipofiilisyyttä kuvaavia kenttiä ja WaterMap-ohjelman tuottamia vesimolekyylien sijaintia ja energiaa havainnollistavia kenttiä yhdessä lukuisten ligandia kuvaavien 2D- ja 3D-deskriptorien kanssa. Malleja sovellettiin kinaasien ja seriiniproteaasien selektiivisyysprofiilien tutkimukseen. Tutkimuksen ensimmäisessä osassa kenttäpohjaista PCM-mallinnusta sovellettiin proteiinikinaaseihin, joille selektiivisten inhibiittorien suunnittelu on haastavaa samankaltaisten ATP sitoutumistaskujen takia. Tutkimuksemme osoitti menetelmän soveltuvan kinaasien sitoutumisaffiniteettia ja selektiivisyyttä ohjaavien alueiden osoittamiseen. Jatkona 50 kinaasia ja 80 inhibiittoria käsittäneelle alkuperäiselle tutkimukselle rakensimme kenttäpohjaisia PCM-luokittelumalleja suuremmalle joukolle kinaaseja (95) ja inhibiittoreita (1572) erotellaksemme aktiiviset ja inaktiiviset ligandit toisistaan. Ulkoisen testiyhdiste- tai testikinaasijoukon bioaktiivisuuksien ennustaminen yli 80 % tarkkuudella (Matthews korrelaatiokerroin, MCC noin 0,50) ja ROC-käyrän alle jäävä ala (AUC) yli 0,8 yhdessä aktiivisuutta tukevien alueiden visuaalisen tarkastelun kanssa osoittivat kenttäpohjaisen PCM:n pystyvän tuottamaan sekä ennustavia että visuaalisesti ymmärrettäviä malleja. Tutkimuksen toisessa osassa metodin soveltaminen seriiniproteaaseihin tuotti yleisnäkemyksen sitoutumistaskun eri osien spesifisyyksistä, mikä on ensiarvoisen tärkeää inhibiittorien suunnittelulle. Lisäksi kentistä johdettuja, proteiinien päällekkäinasettelusta riippumattomia Zernike-deskriptoreita hyödynnettiin PCM-malleissa arvioidaksemme proteiinien päällekkäinasettelun vaikutusta kenttien vertailuun ja sen jälkeiseen PCM-mallinnukseen

Identification of structure activity relationships in primary screening data of high-throughput screening assays

The aim of the thesis was to identify structure activity relationships (SAR) in the primary screening data of high-throughput screening (HTS) assays. The strategy was to perform a hierarchical clustering of the molecules, assign the primary screening data to the created clusters and derive models from the clusters. The models should serve to identify singletons, clusters enriched with actives, not confirmed hits and false-negatives. Two hierarchical clustering algorithms, NIPALSTREE and hierarchical k-means have been developed and adapted for this purpose, respectively. A graphical user interface (GUI) has been implemented to extract SAR from the clustering results. Retrospective and prospective applications of the clustering approach were performed. SAR models were created by combining the clustering results with different chemoinformatic methods. NIPALSTREE projects a data set onto one dimension using principle component analysis. The data set is sorted according to the scoring vector and split at the median position into two subsets. The algorithm is applied recursively onto the subsets. The hierarchical k-means recursively separates a data set into two clusters using the k-means algorithm. Both algorithms are capable of clustering large data sets with more than a million data points. They were validated and compared to each other on the basis of different structural classes. NIPALSTREE provided with the loading vectors first insights into SAR whereas the hierarchical k-means yielded superior results. A GUI was developed allowing the display of and the navigation in the clustering results. Functionalities were integrated to analyse the clusters in the dendrogram, molecules in a cluster, and physicochemical properties of a molecule. Measures were developed to identify clusters enriched with actives, to characterize singletons and to analyse selectivity and specificity. Different protease inhibitors of the COBRA database were examined using the hierarchical k-means algorithm. Supported by similarity searches and nearest neighbour analyses thrombin inhibitor singletons were quickly isolated and displayed in the dendrogram. By scaling enrichment factors to the logarithm of the dendrogram level, clusters enriched with different structural classes of factor Xa inhibitors were simultaneously identified. The observed co-clustering of other protease inhibitors provided a deeper insight into selectivity and specificity and shows the utility of the approach for constructing focussed screening libraries. Specificity was analyzed by extracting and clustering relative frequencies of the protease inhibitors from the clusters of dendrogram level 7. A unique ligand based point of view on the pocketome of the protease enzymes was obtained. To identify not confirmed hits and false-negatives in the primary screening data of HTS assays, three assays were retrospectively analysed with the hierarchical k-means algorithm. A rule catalogue was developed judging hits in terminal clusters based on the cluster size, the percent control values of the entries in a cluster, the overall hit rate, the hit rate in the cluster and the environment of a cluster in the dendrogram. It resulted in the identification of a high proportion of not confirmed hits and provided for each hit a rating in context of related non-hits. This allows prioritizing compounds for follow-up studies. Non-hits and hits were retrieved from terminal clusters containing hits. Molecules bearing false-negative scaffolds were co-extracted and enriched. To minimize the number of false-positives in the extracted lists, Bayesian regularized artificial neutral network classification models were trained with the data. Applying the models marked improvement of enrichment factors for the false-negatives was obtained. It proofs the scaffold-hopping potential of the approach. NIPALSTREE, the hierarchical k-means algorithm and self-organising maps were prospectively applied to identify novel lead candidates for dopamine D3 receptors. Compounds with novel scaffolds and low nanomolar binding affinity (65 nM, compound 42) were identified. To provide a deeper insight into the SAR of these molecules, different alternative computational methods were employed. Support vector-based regression and partial least squares were examined. Predictive models for dopamine D2 and D3 receptor binding affinity values were obtained. Important features explaining SAR were extracted from the models. The prospective application of the models to the diverse and novel virtual screening data was of limited success only. Docking studies were performed using a homology model of the dopamine D3 receptor. The visual inspection of the binding modes resulted in the hypothesis of two alternative binding pockets for the aryl moiety of dopamine D3 receptor antagonists. A pharmacophore model was created simultaneously requiring both aryl moieties. Virtual screening with the model identified a nanomolar hit (65 nM, compound 59) corroborating the hypothesis of the two binding pockets and providing a new lead structure for dopamine D3 receptors. The presented data shows that the combined approach of hierarchically clustering a data set in combination with the subsequent usage of the clusters for model generation is suited to extract SAR from screening data. The models are successful in identifying singletons, clusters enriched with actives, not confirmed hits and false-negative scaffolds.Das Ziel der Arbeit war es, Struktur-Aktivitätsbeziehungen (SAR) in primären Screeningdaten von Hochdurchsatzscreening (HTS)- Assays zu finden. Als Strategie sollten die Moleküle hierarchisch geclustert werden, die primären Screeningdaten den gebildeten Clustern zugeordnet und Modelle aus den Clustern abgeleitet werden. Die Modelle sollten das Auffinden von Singletons, mit Hits angereicherter Cluster, nicht bestätigter Hits und falsch Negativer ermöglichen. Zu diesem Zweck wurden zwei hierarchische Clusteralgorithmen, NIPALSTREE und hierarchischer k-means, entwickelt bzw. angepasst. Eine graphische Benutzeroberfläche (GUI) wurde implementiert, um SAR aus den Ergebnissen der Clusterung abzuleiten. Retrospektive und prospektive Anwendungen wurden mit den Clusteransätzen verfolgt. SAR Modelle wurden durch Verwendung der Ergebnisse der Clusterung mit verschiedenen chemoinformatischen Verfahren erstellt. NIPALSTREE projiziert mit Hilfe der Hauptkomponentenanalyse einen Datensatz auf eine Dimension. Der Datensatz wird anhand des Scoringvektors sortiert und, basierend auf dem Median, in zwei Teilmengen aufgetrennt. Der Algorithmus wird rekursiv auf die neu gebildeten Mengen angewandt. Der hierarchische k-means Algorithmus trennt, basierend auf dem k-means Algorithmus, einen Datensatz rekursiv in zwei Cluster auf. Beide Algorithmen sind in der Lage, große Datenmengen mit mehr als einer Million Datenpunkte zu clustern. Sie wurden anhand verschiedener Strukturklassen validiert und miteinander verglichen. NIPALSTREE erbrachte mit dem Loadingvektor erste Einblicke in die SAR, wohingegen der hierarchische k-means zu besseren Ergebnissen führte. Eine GUI wurde entwickelt, die es erlaubt, die Clusterergebnisse darzustellen und darin zu navigieren. Funktionalitäten wurden bereitgestellt, um die Cluster im Dendrogramm, die Moleküle eines Clusters und die physikochemischen Eigenschaften eines Moleküls zu analysieren. Verfahren wurden entwickelt, um mit Hits angereicherte Cluster zu finden, Singletons zu charakterisieren und Selektivität und Spezifität zu analysieren. Verschiedene Proteaseinhibitoren aus der COBRA-Datenbank wurden mit dem hierarchischen k-means Algorithmus näher betrachtet. Mit Hilfe von Ähnlichkeitssuchen und nächsten Nachbaranalysen wurden Thrombininhibitorsingletons im Dendrogram in kürzester Zeit isoliert und dargestellt. Cluster, die mit verschiedenen Strukturklassen von Faktor-Xa-Inhibitoren angereichert waren, wurden, durch Skalierung des Anreicherungsfaktors auf den Logarithmus der Dendrogrammebene, gleichzeitig im Dendrogramm identifiziert. Eine Clusterung der Faktor-Xa-Inhibitoren mit anderen Proteaseinhibitoren wurde beobachtet. Sie erbrachte einen vertieften Einblick in Selektivität und Spezifität und zeigt die Anwendbarkeit des Ansatzes zur Erstellung fokussierter Screeningbibliotheken. Durch Extrahierung und Clusterung der relativen Anteile der Proteaseinhibitoren aus den Clustern von Dendrogrammebene sieben wurde die Spezifität der Proteaseinhibitoren analysiert. Eine spezifische, Liganden basierte Betrachtung des Pocketoms der Proteaseenzyme wurde erhalten. Um nicht bestätigte Hits und falsch Negative in den primären Screening Daten von HTS Assays zu finden, wurden drei Assays in Retrospektive mit dem hierarchischen k-means analysiert. Ein Regelwerk wurde entwickelt, welches Hits anhand der Clustergröße, des Prozent-Kontrollwertes der Einträge eines Clusters, der Gesamthitrate, der Hitrate in einem Cluster und der Umgebung des Clusters im Dendrogramm bewertet. Das Regelwerk führte zum Auffindung eines großen Anteils nicht bestätigter Hits. Zudem wurde für jeden Hit eine Bewertung im Kontext verwandter Nichthits erhalten. Dies erlaubt ein Priorisieren von Molekülen für Folgeuntersuchungen. Nichthits und Hits wurden aus Endcluster, die Hits enthielten, extrahiert. Moleküle mit falsch negativen Molekülgrundgerüsten wurden koextrahiert und angereichert. Um falsch Positive in den extrahierten Listen zu minimieren, wurden Bayesische regularisierte neuronale Klassifizierungsnetze mit den Daten trainiert. Die Anwendung der Modelle ergab eine deutliche Verbesserung der Anreicherungsfaktoren der falsch Negativen. Es zeigt, dass die Methode in der Lage ist, einen Molekülgrundgerüstwechsel durchzuführen. NIPALSTREE, der hierarchische k-means und selbst organisierende Karten wurden prospektiv angewandt, um neue Leitstrukturkandidaten für Dopamin-D3-Rezeptoren zu finden. Moleküle mit neuen Molekülgrundgerüsten und Bindungsaffinitäten im niedrigen nanomolaren Bereich wurden gefunden (65 nM für Molekül 42). Um einen tieferen Einblick in die SAR dieser Moleküle zu erhalten, wurden verschiede Computerverfahren verwendet. Supportvektorregression und PLS („partial least squares“) wurden untersucht. Es war möglich, voraussagende Modelle für Dopamin-D2 und D3 Bindungsaffinitäten zu erstellen. Die SAR erklärende Moleküleigenschaften konnten aus den Modellen extrahiert werden. Die prospektive Anwendung der Modelle auf die diversen und neuen virtuellen Screeningdaten war nur von begrenztem Erfolg. Dockingstudien wurden mit einem Homologiemodell des Dopamin-D3-Rezeptors durchgeführt. Die visuelle Begutachtung der Bindemoden führte zur Hypothese zweier alternativer Bindetaschen für den Aryl-Rest von Dopamin-D3-Rezeptorantagonisten. Ein Pharmakophormodell wurde erstellt, welches beide Aryl-Reste gleichzeitig benötigt. Ein virtuelles Screening mit dem Modell identifizierte einen nanomolaren Hit (65 nM für Molekül 59), welcher die Hypothese unterstützt und eine neue Leitstruktur für Dopamin-D3-Rezeptoren darstellt. Die vorgestellten Daten zeigen, dass der kombinierte Ansatz aus hierarchischer Clusterung und anschließender Verwendung der Cluster zur Modellerstellung, SAR in HTS-Daten findet. Die Modelle sind geeignet zum Auffinden von Singletons, mit Hits angereichter Cluster, nicht bestätigter Hits und falsch negativer Molekülgrundgerüste

Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

Kinetics of environmental biocomplexity : experiments, quantum chemistry and machine learning

Tese (doutorado) — Universidade de Brasília, Instituto de Química, Programa de Pós-Graduação em Química, 2022.Micro poluentes de preocupação emergente têm imposto um grande desafio tecnológico: pesticidas, drogas e outras substâncias antropogênicas são cada vez mais encontrados em ambientes aquáticos e atmosféricos e até mesmo no abastecimento de água, estando relacionados a efeitos adversos sobre a biota e a saúde humana. Superar esse desafio requer a compreensão do comportamento dessas espécies no meio ambiente e o desenvolvimento de tecnologias que permitam minimizar sua disseminação. Alternativas viáveis aplicadas nesta tese incluem o uso de processos de oxidação baseado em radicais utilizando tanto o método experimental – através do método cinético de competição – quanto os protocolos teóricos – um conjunto de cálculos cinéticos, quânticos e aprendizado de máquina. Em um primeiro estudo, os mecanismos, cinéticas e uma avaliação da toxicidade da degradação do picloram – pesticida amplamente utilizado no mundo – iniciados por radicais OH indicam que: i) duas vias favoráveis ocorrem por adição ao anel de piridina, ii) picloram e a maioria dos produtos de degradação são estimados como prejudiciais; no entanto, ii) esses compostos podem sofrer fotólise pela luz solar. No entanto, o método cinético da competição e a descrição da química quântica fazem da degradação uma empreendimento formidável, considerando os custos de equipamentos instrumentais ad hoc e esforços computacionais dedicados. Para superar os exigentes procedimentos convencionais, desenvolvemos uma aplicação web gratuita e de fácil acesso (www.pysirc.com.br) baseada no aprendizado de máquina holístico combinado com modelos de impressões digitais moleculares que permitem a compilação de parâmetros cinéticos e interpretação mecanicista de ataques de oxidação baseado em radicais de acordo com os princípios da OCDE. Algoritmos de aprendizagem de máquina foram implementados, e todos os modelos forneceram alto desempenho de ajuste para a degradação baseado em radical no ambiente aquático e atmosférico. Os modelos foram interpretados utilizando o método SHAP (Explicações Aditivas de SHapley): os resultados mostraram que o modelo desenvolvido fez a previsão com base em uma compreensão razoável de como grupos de retirada/doação de elétrons interferem na reatividade dos radicais. Argumentamos que nossos modelos e interface web podem estimular e expandir a aplicação e interpretação de pesquisas cinéticas sobre contaminantes em unidades de tratamento de água e ar com base em tecnologias oxidativas avançadas.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Fundação de Apoio à Pesquisa do Distrito Federal (FAP/DF) e Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG).Micro-pollutants of emerging concern have imposed a major technological challenge: pesticides, drugs and other anthropogenic substances are increasingly found in aquatic and atmospheric environments and even in water supplies, being related to adverse effects on biota and human health. Overcoming this challenge requires understanding the behavior of these species in the environment and the development of technologies that allows for minimizing their dissemination. Viable alternatives applied in this thesis include the use of radical-based oxidation processes using both experimental – via the competition kinetics method – and theoretical protocols – blend of kinetic, quantum chemistry and machine learning calculations. In a first study, the mechanisms, kinetics, and an evaluation of the toxicity of picloram degradation – a pesticide widely used in the world - initiated by OH radicals indicate that: i) two favorable pathways occur by addition to the pyridine ring, ii) picloram and the majority of degradation products are estimated as harmful; however, ii) these compounds can suffer photolysis by sunlight. However, the competition kinetic method and the quantum chemistry description make the degradation analyses a formidable enterprise, considering the costs of ad hoc instrumental equipment’s and dedicated computational efforts. To overcome the demanding conventional procedures, we developed a free and user-friendly web application (www.pysirc.com.br) based on holistic machine learning combined with molecular fingerprints models that permits compilation of kinetic parameters and mechanistic interpretation of radical-based oxidation attacks according to the OECD principles. Machine learning algorithms were implemented, and all models provided high goodness-of-fit for radical-based degradation in aquatic and atmospheric environment. The models were interpreted using the SHAP (SHapley Additive exPlanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing/donating groups interfere in the reactivity of the radicals. We argue that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water and air treatment units based on advanced oxidative technologies