Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs

Multiple machine learning methods aided virtual screening of Na(V)1.5 inhibitors

Na(v)1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na(v)1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na(v)1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na(v)1.5 ion channel and key privileged substructures with high affinity were also extracted.Peer reviewe

Molecular similarity searching based on deep learning for feature reduction

The concept of molecular similarity has been widely used in rational drug design, where structurally similar molecules are explored in molecular databases for retrieving functionally similar molecules. The most used conventional similarity methods are two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the effectiveness of similarity searching methods. Moreover, the majority of existing similarity searching methods often disregard the importance of some features over others and assume all features are equally important. Thus, this study proposed three approaches for identifying the important features of molecules in chemical datasets. The first approach was based on the representation of the molecular features using Autoencoder (AE), which removes irrelevant and redundant features. The second approach was the feature selection model based on Deep Belief Networks (DBN), which are used to select only the important features. In this approach, the DBN is used to find subset of features that represent the important ones. The third approach was conducted to include descriptors that complement to each other. Different important features from many descriptors were filtered through DBN and combined to form a new descriptor used for molecular similarity searching. The proposed approaches were experimented on the MDL Data Drug Report standard dataset (MDDR). Based on the test results, the three proposed approaches overcame some of the existing benchmark similarity methods, such as Bayesian Inference Networks (BIN), Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP) and Quantum-Based Similarity Method (SQB). The results showed that the performance of the three proposed approaches proved to be better in term of average recall values, especially with the use of structurally heterogeneous datasets that could produce results than other methods used previously to improve molecular similarity searching

Multiple Screening Techniques: A Way to Develop a Chemical-Animal Model

The primary objective and public health relevance of this investigation was to develop a chemical-animal model with a toxicological and therapeutic approach. The results outlined here are developed from the latest techniques being employed in the chemical and biomedical fields. This research outlines a model building approach that progressed from a preliminary agent screening technique (quantitative structure-activity relationship/structure-activity relationship, QSAR/SAR) and in vivo testing using the Chernoff-Kavlock (CK) assay through to in vitro testing in transgenic adenocarcinoma of the mouse prostate (TRAMP) cell lines. The preliminary investigation involved development of a QSAR/SAR model to predict the teratogenicity of a series of related chemical agents (dopamine mimetics). This QSAR/SAR model was then validated using a complete leave one out cross-validation. The predictivity of a more general QSAR/SAR model of developmental toxicity was then tested experimentally in vivo using the chemical agent retinoic acid. The second model was based on in vivo animal screening using the CK assay. The CK assay involves the dosing of pregnant animals, either mice or rats, during the organogenesis period of fetal development. This assay quantitatively measures effects on fetal viability and growth, and allows for a more qualitative assessment of teratogenicity by recording obvious malformations. The third segment of this study was an in vitro evaluation of the effects of a series of microtubule perturbing agents on cell viability, cell death and gene expression of the TRAMP cell lines. This research could contribute to the development of drug treatments that would be more effective against human prostate cancer.In the first section of my thesis, a mathematical model was generated with experimental data from the literature on a congeneric series of twelve dopamine mimetics. Based on a single physicochemical parameter, the final model is 100% effective at predicting biological activity (teratogenicity) of dopamine mimetics. We also found inconsistencies in the original biological data that might influence the choice of final model.The second section of my thesis involves the experimental validation of a general QSAR/SAR model that predicted retinoic acid would be positive for developmental toxicity. Retinoic acid was therefore tested in a standard mouse CK assay (the same assay used to generate the data used to generate the model) to test the SAR model prediction. Significant increases in the incidence of both fetal death and intrauterine growth retardation were observed in the offspring of the treated mice. Statistical analysis revealed these effects were dose-dependent. These results demonstrated, in a quantitative manner, the developmental toxic effects of retinoic acid in the mouse, as were predicted by the SAR model and as expected from developmental literature.The final segment of my thesis dealt with the preliminary in vitro screening of four promising anticancer agents, Analog II, 4-methoxy Analog II, JR oxime I and TDH 169 on the clonal TRAMP cell lines C1A, C2H and C2N. 4-Methoxy Analog II displayed the most promising antiproliferative effects and apoptosis inducing effects. A microarray analysis of mRNA expression in response to 4-methoxy Analog II was conducted to determine agent-induced expression alterations in the C1A cell line. Upregulation of the apoptosis activating genes Bok and Siva-pending was observed, while the apoptosis inhibiting genes Birc 4, Dad1 and Atf5 were significantly downregulated

Efficacy of different protein descriptors in predicting protein functional families using support vector machine

Master'sMASTER OF SCIENCE (PHARMACY

Exploring Molecular Diversity: There is Plenty of Room at Markush's

L'estratègia de les etapes inicials del descobriment de fàrmacs està normalment basada en un procés anomenat hit-to-lead que implica un extens estudi entorn de la síntesi de derivats d'una molècula original que prèviament hagi mostrat certa activitat biològica davant d'una diana concreta. Per tant, aquest procés comporta la síntesi de molts anàlegs que descriurien una subquimioteca, que generalment evidencia que aquests estudis estan molt focalitzats al voltant de l'espai químic del compost original. Així i tot, quan aquesta molècula és finalment patentada, es descriu un espai químic molt més vast per mitjà d'estructures Markush donant per suposat que alguns dels seus derivats puguin presentar també activitat biològica. Tot i això, la presència d'aquestes estructures no implica la síntesi comprovada de tota la biblioteca molecular sinó només una petita mostra de la mateixa. La nostra hipòtesi és que hi ha una gran part de l’espai químic d’aquestes biblioteques que està sense explorar i pot amagar possibles candidats que poden fins i tot superar l’activitat del hit original. A través d'aquest projecte, es proposa una alternativa que sosté que una selecció racional de poques molècules – basat en l'agrupament segons semblança molecular – pot representar de manera més significativa l'espai químic establert, oferint la possibilitat d'explorar regions desconegudes que podrien amagar més potencial biològic. Després de revisar els darrers fàrmacs aprovats per la FDA en el període del 2008 al 2020 i la base de dades de molècules bioactives de ChEMBL, s'ha dut a terme una exploració de l'ampli espai químic resultant de molècules petites amb propietats similars a les dels medicaments per definir nous espais accessibles que podrien ocultar activitat. Els resultats obtinguts de set casos d'estudis reals han demostrat que tant la selecció racional com l’aleatòria representen més significativament les biblioteques combinatòries declarades a les patents, que les molècules descrites fins ara. S'han realitzat dos estudis pràctics que implementen aquesta metodologia suggerida per descriure millor l'espai químic del fàrmac antipalúdic Tafenoquina i del Dacomitinib, un inhibidor de tirosina cinases de segona generació per al tractament del càncer de pulmó de cèl·lules no petites. L’exploració de l’espai químic d’aquestes dues famílies ha portat a la síntesi racional de set anàlegs antipalúdics i vuit inhibidors de cinases que han mostrat interessants activitats inhibidores. Aquests resultats demostren que l'aplicació de la quimioinformàtica per a la selecció de biblioteques pot millorar la capacitat d'inspeccionar millor els conjunts de dades químiques per identificar nous compostos precandidats i representar grans biblioteques per a posteriors campanyes de reposicionament.La estrategia de las etapas iniciales del descubrimiento de fármacos está normalmente basada en un proceso denominado hit-to-lead que implica un extenso estudio entorno a la síntesis de derivados de una molécula original que previamente haya expresado cierta actividad biológica frente a una diana concreta. Por ende, este proceso conlleva la síntesis de muchos análogos que describirían una sublibrería química, la cual generalmente evidencia que estos estudios están muy focalizados alrededor del espacio químico del compuesto original. Aún y así, cuando esta molécula es finalmente patentada, se describe un espacio químico mucho más vasto por medio de estructuras Markush teorizando que algunos de sus derivados puedan presentar también actividad biológica. Sin embargo, la presencia de estas estructuras no implica la síntesis comprobada de toda la biblioteca molecular sino solo una pequeña muestra de la misma. Nuestra hipótesis es que hay una gran parte del espacio químico de estas bibliotecas que está sin explorar y puede ocultar posibles candidatos que pueden hasta superar la actividad del hit original. A través de este proyecto, se propone una alternativa que sostiene que una selección racional de pocas moléculas – fundada en el agrupamiento según su similitud química – puede representar de manera más significativa el espacio químico establecido, ofreciendo la posibilidad de explorar regiones desconocidas que podrían ocultar más potencial biológico. Después de revisar los últimos fármacos aprobados por la FDA en el período de 2008 a 2020 y la base de datos de moléculas bioactivas de ChEMBL, se ha llevado a cabo una exploración del amplio espacio químico resultante de moléculas pequeñas con propiedades similares a las de los medicamentos para definir nuevos espacios accesible que podrían ocultar actividad. Los resultados obtenidos de siete casos de estudios reales han demostrado que tanto la selección racional como la aleatoria representan más significativamente las bibliotecas combinatorias declaradas en las patentes que las moléculas descritas hasta la fecha. Se han desarrollado dos estudios prácticos que implementan esta metodología sugerida para describir mejor el espacio químico del fármaco antipalúdico Tafenoquina y Dacomitinib, un inhibidor de la tirosina quinasa de segunda generación para el tratamiento del cáncer de pulmón de células no pequeñas. La exploración del espacio químico de estas dos familias ha llevado a la síntesis racional de siete análogos antipalúdicos y ocho inhibidores de quinasas que han mostrado interesantes actividades inhibidoras. Estos resultados demuestran que la aplicación de la quimioinformática para la selección de bibliotecas puede mejorar la capacidad de inspeccionar mejor los conjuntos de datos químicos para identificar nuevos potenciales hits y representar grandes bibliotecas para fines de reposicionamiento.The early Drug Discovery strategy is commonly based on a hit-to-lead process which involves large research on the synthesis of derivatives of an original molecule that had previously shown biological activity against a specific biological target. Therefore, this process implies the synthesis of many analogs leading to the description of a chemical sub-library which generally leads to a highly focused study on the chemical space nearby the hit compound. However, when this drug is finally patented, a wider chemical space derived from a Markush structure is described, theorizing that some analogs within may present biological activity. Nevertheless, this claim involving the Markush structure does not imply the proven synthesis of all the chemical library but just a small population of it. We hypothesize that there is a great part of the chemical space of these libraries that is unexplored and can hide potential lead candidates which may even surpass the activity of the original hit. Through this project, an alternative is proposed claiming that a rational selection of a short sample of small molecules – founded on similarity-based clustering – can represent more significatively the stated chemical space offering the possibility to explore the unknown space that could hide more potential biological activity. After a review on the latest approved drugs by the FDA in the period from 2008 to 2020 and the ChEMBL database of bioactive molecules, an exploration of the resulting wide chemical space of small molecules with drug-like properties has been assessed in order to define accessible spots that might hide biological activity. The obtained results from seven real cases of study have proven that random and rationally selected molecules represent more significantly the combinatorial libraries stated in the patents rather than the reported molecules until date. Furthermore, two practical studies implementing our suggested methodology have been developed to better describe the chemical space of the antimalarial drug Tafenoquine and Dacomitinib, a second-generation tyrosine kinase inhibitor for non-small-cell lung cancer treatment. The assessment driven by a better chemical space exploration of these two families have led to the rational synthesis of seven antimalarial analogs and eight kinase inhibitors which have shown interesting inhibitory activities. Our results evince that the application of cheminformatics for library selection may improve the ability to better inspect chemical datasets in order to identify new potential hits and represent large libraries for further reprofiling purposes

Développement de méthodes et d’outils chémoinformatiques pour l’analyse et la comparaison de chimiothèques

Some news areas in biology ,chemistry and computing interface, have emerged in order to respond the numerous problematics linked to the drug research. This is what this thesis is all about, as an interface gathered under the banner of chimocomputing. Though, new on a human scale, these domains are nevertheless, already an integral part of the drugs and medicines research. As the Biocomputing, his fundamental pillar remains storage, representation, management and the exploitation through computing of chemistry data. Chimocomputing is now mostly used in the upstream phases of drug research. Combining methods from various fields ( chime, computing, maths, apprenticeship, statistics, etc…) allows the implantation of computing tools adapted to the specific problematics and data of chime such as chemical database storage, understructure research, data visualisation or physoco-chimecals and biologics properties prediction.In that multidisciplinary frame, the work done in this thesis pointed out two important aspects, both related to chimocomputing : (1) The new methods development allowing to ease the visualization, analysis and interpretation of data related to set of the molecules, currently known as chimocomputing and (2) the computing tools development enabling the implantation of these methods.De nouveaux domaines ont vu le jour, à l’interface entre biologie, chimie et informatique, afin de répondre aux multiples problématiques liées à la recherche de médicaments. Cette thèse se situe à l’interface de plusieurs de ces domaines, regroupés sous la bannière de la chémo-informatique. Récent à l’échelle humaine, ce domaine fait néanmoins déjà partie intégrante de la recherche pharmaceutique. De manière analogue à la bioinformatique, son pilier fondateur reste le stockage, la représentation, la gestion et l’exploitation par ordinateur de données provenant de la chimie. La chémoinformatique est aujourd’hui utilisée principalement dans les phases amont de la recherche de médicaments. En combinant des méthodes issues de différents domaines (chimie, informatique, mathématique, apprentissage, statistiques, etc.), elle permet la mise en oeuvre d’outils informatiques adaptés aux problématiques et données spécifiques de la chimie, tels que le stockage de l’information chimique en base de données, la recherche par sous-structure, la visualisation de données, ou encore la prédiction de propriétés physico-chimiques et biologiques.Dans ce cadre pluri-disciplinaire, le travail présenté dans cette thèse porte sur deux aspects importants liés à la chémoinformatique : (1) le développement de nouvelles méthodes permettant de faciliter la visualisation, l’analyse et l’interprétation des données liées aux ensembles de molécules, plus communément appelés chimiothèques, et (2) le développement d’outils informatiques permettant de mettre en oeuvre ces méthodes