EMPLOYING RECURSIVE PARTITION AND REGRESSION TREE METHOD TO INCREASE THE QUALITY OF STRUCTURE-BASED VIRTUAL SCREENING IN THE ESTROGEN RECEPTOR ALPHA LIGANDS IDENTIFICATION

Objective: Increase the predictive quality of the structure-based virtual screening (SBVS) protocol to identify potent ligands for estrogen receptoralpha (ERα).Methods: Employing recursive partition and regression tree (RPART) method to identify potent ligands for ERα among their decoys by using moleculardocking scores and the protein-ligand interaction fingerprint bitstrings as the predictors. These predictors were obtained from previously publishedSBVS campaign to identify potent ligands for ERα. The quality of the protocol by using RPART method was assessed by examining the enrichmentfactors and the accuracy in 95% level of confidence compared to the reference protocol.Results: The decision tree resulted from analysis using RPART method increased the enrichment factor and the accuracy values of the SBVS protocolfrom 18.5 to 247.9 and from 0.975 to 0.989, respectively. Notably, the accuracy value of the protocol using the decision tree was statistically significantin 95% level of confidence while the reference protocol was not.Conclusion: RPART method could lead to a significant increase of the SBVS quality to identify potent ligands for ERα.Keywords: Recursive partition and regression tree, Molecular docking, Interaction fingerprint, Estrogen receptor alpha.Â

Application of Quantitative Structure–Activity Relationship Models of 5-HT 1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT 1A Ligands

The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs

Cheminformatics Modeling of Diverse and Disparate Biological Data and the Use of Models to Discover Novel Bioactive Molecules

Ligand-based drug design is a popular and efficient computational approach to facilitate the drug discovery process. Current approaches mainly focus on optimizing the computational algorithms to improve the efficiency or accuracy of virtual screening; however, the success of ligand-based drug design relies not only on the effectiveness and robustness of the underlying algorithms, but much more importantly, on the quality of the data for model building. Although numerous chemical probe databases have emerged recently, few evaluation of data quality and reliability have been performed. Building upon our lab's experience in Quantitative Structure-Activity Relationship (QSAR) method and methods developed in the field of cheminformatics, this dissertation focuses on: 1) Investigation and comparison of the predictive power of QSAR methods with other ligand-based drug discovery approaches, such as Similarity Ensemble Approach (SEA) and Prediction of Activity Spectra for Substances (PASS); 2) Using QSAR methods to validate the consistency and reliability of biomedical data in disparate data sources. 3) Developing a novel, rigorous and dataset-specific QSAR workflow for the application on multiple therapeutic targets in order to identify diverse hits with high potency in practical virtual screening projects. These works succeed in thoroughly investigating the current approaches for ligand-based drug discovery, exploring the consistency and quality of major annotated cheminformatics databases, and identifying many pharmaceutically important ligands. The success of our studies harshly challenges some popular multi-target profile prediction methods and contributes to the development of cheminformatics by emphasizing the importance of determining trustworthy data sources.Doctor of Philosoph

Computational Methods for the Integration of Biological Activity and Chemical Space

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

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach

Discovery of new selective antagonists of G-protein coupled receptors of therapeutic interest

GPCR are integral membrane receptor proteins that are characterized by heptahelical transmembrane domains connected by intracellular and extracellular loops. GPCRs are an attractive class of proteins for drug discovery, with more than 50% of all drugs regulating GPCR function, and some 30% of these drugs directly target GPCRs. Despite the number of GPCR crystal structures determined recently, they only represent a small fraction of total number of GPCRs known. Homology modelling has been the methodology used to fill the gap. However, the low sequence similarity between targets and templates hampers these studies. Aimed at overcoming these drawbacks template selection and the refinement process were studied in this work. Thus, several atomistic models of rat M3 muscarinic receptor were constructed from human M2 muscarinic receptor, human histamine 1 receptor and bovine rhodopsin receptor as templates. Moreover, in order to determine the effect of ligand in the simulation system, an extra model of M2 receptor was refined with NMS bound inside and an extra model refined without ligand. Results show the sampling time 500ns is adequate simulation time and molecular dynamics simulation of the protein embedded in a lipid bilayer as a refinement process improves on the homology models. Specifically, the refinement process can correct the length of the TM segment of the target receptor; the accuracy of the model greatly depends on the proximity of the template and the target in the phylogenetic tree and finally, the presence of a ligand produces a faster equilibration of the system. This methodology was used to study the pharmacological profile of bradykinin receptors B1 and B2. The B1 receptor was constructed using the chemokine CXC4 and bovine rhodopsin receptors as templates. Antagonists selected for the docking studies include Compound 11, Compound 12, Chroman28, SSR240612, NPV-SAA164 and PS020990. Analysis of the ligand-receptor complexes permitted the definition of a pharmacophore that describes the stereochemical requirements of antagonist binding. For the B2 receptor, a similar procedure was followed using the same template. In this case, the set of compounds used were Fasitibant, FR173657, Anatibant, WIN64338, Bradyzide, CHEMBL442294, and JSM10292. The outcome of this study is summarized in a 3D pharmacophore that explains the observed structure-activity results and provides insight into the design of novel molecules with antagonistic profile. To prove the validity of the pharmacophoric hypotheses, a virtual screening process was carried out. The results of the binding studies show about a 33% success rate with a correlation between the number of pharmacophore points fulfilled and their antagonistic potency. Some of these structures are disclosed in this thesis. Moreover, the B1R and B2R pharmacophores developed were compared and the observed differences permitted to explain the stereochemical requirements for receptor-selective ligands. The final study of this study was to establish a rational explanation for the role of zinc in preventing the dimerization of the serotonin 5-Hydroxytryptamine 1A receptor (5-HT1A) and Galanin receptor 1 (GALR1) involved in depression. Homology modeling was used to build atomistic models of these receptors using the crystallographic structures of 5-HT1B and κ– opioid receptor, respectively. First, prospective zinc binding sites were identified for the 5-HT1A using a molecular probe. Second, heterodimers of the two receptors were constructed with different interfaces: TM4 and TM5; TM6 and TM7; TM1 and TM2. Analysis of the 12 zinc-binding sites and the heterodimer interfaces suggests that there is a coincidence between zinc binding sites and heterodimerization interfaces providing a rational explanation for the role of zinc in the molecular processes associated with heterodimer preventionLos receptores acoplados a proteínas G (GPCRs) son proteínas de membrana que se caracterizan por dominios transmembrana heptahelicoidales conectados por lazos intracelulares y extracelulares. GPCRs son un atractivo grupo de proteínas para el descubrimiento de nuevos fármacos puesto que más del 50% de los medicamentos en el mercado que regulan su función y alrededor del 30% que tienen un GPCR como diana. A pesar del gran número de estructuras cristalográficas de GPCRs que se han determinado recientemente, estas solamente representan una pequeña fracción del número total de GPCRs. La homología de secuencia se utiliza de forma rutinaria para llenar el vacío, sin embargo, la baja identidad de secuencia entre miembros de esta familia obstaculiza estos estudios. Con el objetivo de superar estos inconvenientes, tanto el proceso de selección de la plantilla, como el proceso de refinamiento del modelo han sido estudiados en este trabajo. Se construyeron modelos atómicos del receptor muscarínico M3 de rata a partir del receptor humano M2 muscarínico, del de histamina humano 1 y de la rodopsina bovina como plantilla. Por otra parte, con el fin de determinar el efecto del ligando en el proceso de refinamiento, el receptor M2 fue refinado con el ligando NMS y además se construyó un modelo sin ligando. Los resultados muestran que un tiempo de muestreo 500ns es adecuado y que la dinámica molecular representa un proceso de refinamiento adecuado. Esta metodología se utilizó para estudiar el perfil farmacológico de los receptores de bradiquinina B1 y B2. El receptor B1 se construyó usando los receptores CXC4 de quimoquina y rodopsina bovina como plantillas. Los antagonistas seleccionados para los estudios de anclaje incluyen el Compuesto 11, el Compuesto 12, Chroman28, SSR240612, NVP-SAA164 y PS020990. El análisis de los complejos ligando-receptor permite la definición de un farmacóforo que describe los requisitos estereoquímicos de unión de antagonistas. Para el receptor B2, se siguió un procedimiento similar utilizando las mismas plantillas. En este caso, el conjunto de los compuestos utilizados fueron Fasitibant, FR173657, Anatibant, WIN64338, Bradyzide, CHEMBL442294 y JSM10292. El resultado de este estudio se resume en un farmacóforo 3D que explica los resultados estructura-actividad observados y ofrece información sobre el diseño de nuevas moléculas con el perfil antagonista. Para probar la validez de las hipótesis farmacofóricas, se llevó a cabo un proceso de cribado virtual. Los resultados de los estudios de unión muestran sobre una tasa de éxito del 33% con una correlación entre el número de puntos farmacóforicos cumplido y su potencia antagonista. Algunas de estas estructuras se describen en esta tesis. Por otra parte, los farmacóforos de B1R y B2R desarrollados se compararon y a través de las diferencias observadas explicar los requisitos estereoquımicos para que los ligandos sean selectivos. El estudio final de este trabajo fue el establecer una explicación racional para el papel del zinc en la prevención de la dimerización del receptor de serotonina 5-hidroxitriptamina 1A (5-HT1A) y el receptor galanina 1 (GALR1) que participan en la depresión. Homología de secuencia se utilizó para construir modelos atómicos de estos receptores utilizando las estructuras cristalográficas de los receptores 5-HT 1B y κ de opiáceos, respectivamente. En primer lugar, se identificaron los posibles sitios de unión de zinc para el 5-HT1A usando una sonda molecular. En segundo lugar, los heterodímeros de los dos receptores fueron construidos con diferentes interfaces: TM4 y TM5; TM6 y TM7; TM1 y TM2. El análisis de los 12 sitios de unión de zinc y las interfaces heterodímero sugiere que existe una coincidencia entre los sitios de unión de zinc y las interfaces de heterodimerización que proporcionan una explicación racional para el papel del zinc en los procesos moleculares asociados con la prevención heterodímero.Postprint (published version

A perspective on multi-target drug discovery and design for complex diseases

Diseases of infection, of neurodegeneration (such as Alzheimer's and Parkinson's diseases), and of malignancy (cancers) have complex and varied causative factors. Modern drug discovery has the power to identify potential modulators for multiple targets from millions of compounds. Computational approaches allow the determination of the association of each compound with its target before chemical synthesis and biological testing is done. These approaches depend on the prior identification of clinically and biologically validated targets. This Perspective will focus on the molecular and computational approaches that underpin drug design by medicinal chemists to promote understanding and collaboration with clinical scientists

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Inference of binding affinity from neuronal receptors in humans

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016Only some compounds (e.g. ligands) act as neurotransmitters in the brain, binding to specific neuroreceptors. Understanding the criteria behind why a ligand binds to a particular target in the brain can help design drugs which are more effective. With the help of data-mining techniques, quantitative structure–activity/propriety relationship (QSAR/QSPR(Q (SAR)) models and machine learning methods, a supervised model can be built which can predict binding affinities for any molecule, provided sufficient experimental data is available. Models which can predict binding affinities for specific neuroreceptors were built using three machine learning methods (Random Forests, Support Vector Machines and Least Absolute Shrinkage and Selection Operator) and two sets of molecular descriptors from different chemical toolboxes (Open Babel and CDK). Experimental data was collected to create the database and curated by removing inconsistencies and duplicates. The final dataset had 43901 binding affinity values for 53 human neuroreceptors. In the model building phase, 75% of the dataset was used for training and 25% for validation. The modelling consisted of choosing the most important variables (descriptors) for each neuroreceptor and validating using statistical measures. Random Forests and SVM were the best methods. Random Forests was used to select the most important variables and SVM for the statistical measure. The value of root mean squared error (RMSE) was below 0.214, more than half of the receptors had the percentage of variance explained (PVE) above 50% and Pearson's correlation coefficient was above 0.50, confirming the model had a good fit. Small dataset (below 112 entries) resulted in some models having poor results. RMSE values from validation and modelling parts were similar for the best model resulting in a good therefore can predict the strength of binding between neuroreceptor and neurotransmitter. The values of RMSE for the best models were between 0.087 and 0.201 where the PVE is above 50% and correlation above 0.50. Some molecular descriptors were selected frequently; 46 descriptors appeared in more than 20 neuroreceptors, however only 6 descriptors appeared in all neuroreceptors. The same descriptors are used to identify the same family of neuroreceptors.É importante perceber o critério que determina a ligação entre uma molécula e um recetor específico, em particular no cérebro, onde só alguns compostos atuam como neurotransmissores e ligam-se a neurorecetores especifícos. Os neurotransmissores, dependem da sua estrutura para estabelecerem uma ligação com os neurorecetores. Essa ligação pode ser medida através de valores de binding affinity. É possivel, com ajuda de técnicas de data-mining, métodos de machine learning e de relação quantitativa estrutura-propriedade/atividade (QSAR/QSPR), construir um modelo que consiga prever esses valores de binding affinity, desde que tenhamos toda a informação necessária (propriedades/estrutura da molécula e do neurorecetor e valores de binding affinity). Métodos de QSAR/QSPR foram desenvolvidos para compreender as propriedades das moléculas, prever a sua estrutura, e a relação entre os descritores moleculares da sua estrutura com as suas propriedades. De modo a prever valores de binding affinity entre neurotransmissores e neurorecetores, neste trabalho foi criada uma base de dados , com seis dimensões referentes a espécies de animais (dimspecie), a referências bibliográficas (dimref) , a diferentes fontes de dados utilizadas para fazer a base de dados (dimsource), a recetores (dimrec) , a moléculas que vão ligar aos recetores (dimlig) e à localização do recetor (dimlocal).Os valores binding affinity foram expressos em pKi. A base de dados foi curada, os duplicados foram removidos, assim como e valores inconsistentes, como por exemplo, todos as entradas sem estrutura do composto (SMILES). A base de dados tinha 198169 valores de binding affinity. Após a construção da base de dados, procedeu-se à escolha específica de dados para construção do modelo QSAR/QSPR, de modo a ter um bom conjunto de dados. Os critérios de escolha, foram os seguintes: os recetores tinham que estar localizados no cérebro (neurorecetores humano), e tinham que se ligar a mais de 50 ligandos. No final, o conjunto de dados tinha 43901 valores de binding affinity entre 0 e 1 para 53 neurorecetores. O conjunto de dados obtido foi dividido em 75% para o conjunto de treino e 25% para conjunto de teste, isto de forma aleatória para cada neurorecetor. Os descritores moleculares para os compostos do conjunto de dados foram desenvolvidos com a ajuda de duas ferramentas OpenBabel e CDK que foram desenvolvidas para perceber a linguagem dos dados químicos. Essas ferramentas permitem procurar, converter, analisar e armazenar dados de modelação molecular e as caraterísticas bioquímicas. Uma molécula pode ser codificada através de fingerprints que possibilita a determinação da similaridade entre duas moléculas. Existem mais de 5000 descritores, como por exemplo, a massa molecular, o número de átomos, entre outros. Para a construção do modelo, foram usados três métodos combinados de machine learning (Random Forests, Support Vector Machines (SVM) e Least Absolute Shrinkage and Selection Operator (LASSO)), na escolha das variáveis mais importantes, ou seja, as que descrevem melhor a ligação entre os ligandos e os neurorecetores. Os métodos usados foram Random Forests e LASSO e depois posteriormente procedeu-se à validação com obtenção de valores de RMSE , do coeficiente de correlação de Pearson e da percentagem da variação explicada (PVE) com a ajuda do SVM e LASSO. O método de SVM reconhece padrões e baseia-se em encontrar, nos dados , instâncias que são capazes de maximizar a separação entre dois pontos. O método Random Forests, reduz a variância da função da predição estimada, usando para esse feito, árvores de regressão e faz média do resultado. O número de árvores usadas foram 500,enquanto LASSO é um método de regressão que envolve uma penalização do tamanho absoluto dos coeficientes de regressão, em que alguns casos serão zero. Em relação à escolha do conjunto de dados, foi usado o método de cross-validation, em que cada combinação de métodos foram corridos cinco vezes e por cada corrida o conjunto de treino foi divido em 75%, para o conjunto de treino e 25% para o conjunto de teste de forma aleatória, para cada neurorecetor. Os resultados obtidos demonstraram que em todos os métodos, com poucas variáveis, os valores de RMSE são elevados, mas chega a um patamar em que quantas mais variáveis são usadas, maior é o valor de RMSE. No entanto, esses valores variam consoante o recetor, pois existem recetores com um baixo valor de RMSE com 4 variáveis, no entanto, temos outros que são necessários 100 variáveis para se obter um valor baixo de RMSE. O número de variáveis mais importantes para o modelo varia entre 4 e 100. A melhor combinação de métodos em que foram obtidos os melhores resultados para os modelos foram o Random Forests e SVM, apesar de haver três modelos que obtiveram melhores resultados com outro método (LASSO e SVM) . Para validação do modelo foi usado o conjunto de teste que tem 25% dos dados do conjunto de dados iniciais. O RMSE é um bom indicador da qualidade do modelo, mede a distância entre os dados observados e os dados que fazem o modelo. O maior valor de RMSE para o conjunto de treino foi de 0.214. Em geral estamos na presença de bons modelos, no entanto, alguns modelos apresentaram resultados fracos, em que os valores de RMSE são elevados, os valores de PVE e de correlação são baixos e os resultados entre os dados de treino e os dados de testes são muito diferentes, isso acontece na maior partes das vezes quando o número de dados no conjunto de dados é inferior a 112. Para ter um bom modelo, o conjunto de dados precisa de ter mais de 112 entradas, ou seja, é preciso mais de 112 valores de binding affinity para poder construir um bom modelo para esse neurorecetor de modo a prever corretamente valores de binding affinity com outros neurotransmissores . Em relação à correlação que nos indica a força e direção da relação linear entre variáveis, o valor menor é 0, o que indica um fraca correlação, mas em média os valores da correlação são acima de 0.50, o que indica uma forte correlação. A outra medida usada para medir a qualidade do modelo obtido foi a percentagem de variação explicada (PVE) , que em geral está acima do 50%. Os resultados do conjunto de teste foram próximos aos obtidos com o conjunto de treino. Como por exemplo, no caso do modelo para o transportador de seratonina (5-HT transporter), em que o valor de RMSE é 0.216 e a percentagem de variação explicada de 51.1 e para a correlação 0.711, que em comparação com o conjunto de treino que foram 0.196, 57.3 e 0.759 respetivamente são próximos. Os melhores modelos têm os valores de RMSE entre 0.087 e 0.201, em que o PVE está acima de 50% e a correlação está acima de 0.50. Relativamente à selecão dos descritores moleculares mais importantes para a construção do modelo, verificou-se que cerca de 46 descritores moleculares foram escolhidos em pelo menos 20 recetores, isso demonstra que esses descritores são necessários para construir um bom modelo. No entanto, constatou-se que 6 descritores foram selecionados em todos os recetores, a massa molecular, a refratividade molar, o logaritmo do coeficiente partição da água/octanol, o número de ligações simples e aromáticas, demonstrando que estes descritores são os mais importantes para termos um bom modelo. Verificou-se também que os mesmos descritores servem para identificar as mesmas famílias de recetores. Futuramente este modelo pode ser usado na fase inicial da descoberta e produção de novas drogas, pois este modelo consegue verificar a viabilidade dessa droga antes de se proceder a ensaio experimental , através da previsão de valores de binding affinity entre a droga e o seu alvo. O desenvolvimento de uma aplicação online onde se coloca o composto e essa aplicação verifica se o composto se vai ligar a algum neurorecetor

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions