An activity prediction model using shape-based descriptor method

Similarity searching, the activity of an unknown compound (target) is predicted through the comparison of an unknown compound with a set of known activities of compounds. The known activities of the most similar compounds are assigned to the unknown compound. Different machine learning methods and Multilevel Neighborhoods of Atoms (MNA) structure descriptors have been applied for the activities prediction. In this paper, we introduced a new activity prediction model with Shape-Based Descriptor Method (SBDM). Experimental results show that SBDM-MNA provides a useful method of using the prior knowledge of target class information (active and inactive compounds) of predicting the activity of orphan compounds. To validate our method, we have applied the SBDM-MNA to different established data sets from literature and compare its performance with the classical MNA descriptor for activity prediction

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle

Hybrid-enhanced siamese similarity models in ligand-based virtual screen

Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is one of the primary tasks in VS that estimates a molecule's similarity. It is predicated on the idea that molecules with similar structures may also have similar activities. Many techniques for comparing the biological similarity between a target compound and each compound in the database have been established. Although the approaches have a strong performance, particularly when dealing with molecules with homogenous active structural, they are not enough good when dealing with structurally heterogeneous compounds. The previous works examined many deep learning methods in the enhanced Siamese similarity model and demonstrated that the Enhanced Siamese Multi-Layer Perceptron similarity model (SMLP) and the Siamese Convolutional Neural Network-one dimension similarity model (SCNN1D) have good outcomes when dealing with structurally heterogeneous molecules. To further improve the retrieval effectiveness of the similarity model, we incorporate the best two models in one hybrid model. The reason is that each method gives good results in some classes, so combining them in one hybrid model may improve the retrieval recall. Many designs of the hybrid models will be tested in this study. Several experiments on real-world data sets were conducted, and the findings demonstrated that the new approaches outperformed the previous method

Fusion of molecular representations and prediction of biological activity using convolutional neural network and transfer learning

Basic structural features and physicochemical properties of chemical molecules determine their behaviour during chemical, physical, biological and environmental processes and hence need to be investigated for determining and modelling the actions of the molecule. Computational approaches such as machine learning methods are alternatives to predict physiochemical properties of molecules based on their structures. However, limited accuracy and error rates of these predictions restrict their use. This study developed three classes of new methods based on deep learning convolutional neural network for bioactivity prediction of chemical compounds. The molecules are represented as a convolutional neural network (CNN) with new matrix format to represent the molecular structures. The first class of methods involved the introduction of three new molecular descriptors, namely Mol2toxicophore based on molecular interaction with toxicophores features, Mol2Fgs based on distributed representation for constructing abstract features maps of a selected set of small molecules, and Mol2mat, which is a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. The second class of methods was based on merging multi-CNN models that combined all the molecular representations. The third class of methods was based on automatic learning of features using values within the neurons of the last layer in the proposed CNN architecture. To evaluate the performance of the methods, a series of experiments were conducted using two standard datasets, namely MDL Drug Data Report (MDDR) and Sutherland datasets. The MDDR datasets comprised 10 homogeneous and 10 heterogeneous activity classes, whilst Sutherland datasets comprised four homogeneous activity classes. Based on the experiments, the Mol2toxicophore showed satisfactory prediction rates of 92% and 80% for homogeneous and heterogeneous activity classes, respectively. The Mol2Fgs was better than Mol2toxicophore with prediction accuracy result of 95% for homogeneous and 90% for heterogeneous activity classes. The Mol2mat molecular representation had the highest prediction accuracy with 97% and 94% for homogeneous and heterogeneous datasets, respectively. The combined multi-CNN model leveraging on the knowledge acquired from the three molecular presentations produced better accuracy rate of 99% for the homogeneous and 98% for heterogeneous datasets. In terms of molecular similarity measure, use of the values in the neurons of the last hidden layer as the automatically learned feature in the multi-CNN model as a novel molecular learning representation was found to perform well with 88.6% in terms of average recall value in 5% structures most similar to the target search. The results have demonstrated that the newly developed methods can be effectively used for bioactivity prediction and molecular similarity searching

Machine-learning approaches in drug discovery: methods and applications

During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve function-ally similar molecules. The most used conventional similarity methods used 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 performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching

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

Avaliação in silico de fármacos comerciais para reposicionamento na terapêutica da tuberculose

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Ciências da Saúde, Brasília, 2020.A Tuberculose é uma doença infecciosa causada pela Mycobacterium tuberculosis, agente etiológico que foi descoberto em 1882 por Robert Koch e que até os dias atuais segue como um problema de saúde pública em diversos países, entre eles o Brasil. O reposicionamento de fármacos é um grande aliado na investigação por novas alternativas terapêuticas utilizando- se de substâncias que já passaram por todas as fases do desenvolvimento e já são aplicadas na clínica, reduzindo não só o tempo de pesquisa, mas também seus custos. Devido à grande capacidade computacional dos dias atuais e o vasto conhecimento das estruturas proteicas e seus comportamentos é possível através de uma série de diferentes algoritmos simular o comportamento de um alvo proteico ao contato com um ligante, duas técnicas que se utilizam disso são o Virtual Screening e o Molecular Docking. Neste trabalho submetidos à Virtual Screening 1813 fármacos comerciais, selecionados a partir base de dados integrity, em três alvos protéicos de interesse à terapia da tuberculose, RNA polimerase (alvo da rifampicina), enoil-ACP- redutase (alvo da isoniazida) e MmpL3 (Mycobacterial membrane protein Large 3). Os melhores resultados foram subimetidos à docagem molecular e à comparação com as principais interações com os ligantes originais dos alvos; No total foram encontrados 28 candidatos com potencial atividade de inibição enzimática.Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis, their etiological agent that was discovered in 1882 by Robert Koch and nowadays remains a public health problem in several countries, including Brazil. Drug Repurposing is a great ally in the investigation for new therapeutic alternatives by using drugs that have already gone through all stages of development and are already applied in the clinic, reducing not only research time but also all their costs. Due to the great computational capacity of today and the vast knowledge of protein structures and their behaviors, it is possible through a series of different computational algorithms to simulate the behavior of a protein target upon their contact with a ligand. Two techniques use this, the Virtual Screening and Molecular Docking. In this work submitted to Virtual Screening 1813 commercial drugs, selected from the Cortellis Integrity database, in three protein targets of interest to tuberculosis therapy, RNA polymerase (target of rifampicin), enoil-ACP-reductase (target of isoniazid) and MmpL3 (Mycobacterial membrane protein Large 3). The best results were subjected to molecular docking and compared to the main interactions with the target's original ligands; In total 28 candidates were found with potential enzyme inhibiting activity

From Knowledgebases to Toxicity Prediction and Promiscuity Assessment

Polypharmacology marked a paradigm shift in drug discovery from the traditional ‘one drug, one target’ approach to a multi-target perspective, indicating that highly effective drugs favorably modulate multiple biological targets. This ability of drugs to show activity towards many targets is referred to as promiscuity, an essential phenomenon that may as well lead to undesired side-effects. While activity at therapeutic targets provides desired biological response, toxicity often results from non-specific modulation of off-targets. Safety, efficacy and pharmacokinetics have been the primary concerns behind the failure of a majority of candidate drugs. Computer-based (in silico) models that can predict the pharmacological and toxicological profiles complement the ongoing efforts to lower the high attrition rates. High-confidence bioactivity data is a prerequisite for the development of robust in silico models. Additionally, data quality has been a key concern when integrating data from publicly-accessible bioactivity databases. A majority of the bioactivity data originates from high- throughput screening campaigns and medicinal chemistry literature. However, large numbers of screening hits are considered false-positives due to a number of reasons. In stark contrast, many compounds do not demonstrate biological activity despite being tested in hundreds of assays. This thesis work employs cheminformatics approaches to contribute to the aforementioned diverse, yet highly related, aspects that are crucial in rationalizing and expediting drug discovery. Knowledgebase resources of approved and withdrawn drugs were established and enriched with information integrated from multiple databases. These resources are not only useful in small molecule discovery and optimization, but also in the elucidation of mechanisms of action and off- target effects. In silico models were developed to predict the effects of small molecules on nuclear receptor and stress response pathways and human Ether-à-go-go-Related Gene encoded potassium channel. Chemical similarity and machine-learning based methods were evaluated while highlighting the challenges involved in the development of robust models using public domain bioactivity data. Furthermore, the true promiscuity of the potentially frequent hitter compounds was identified and their mechanisms of action were explored at the molecular level by investigating target-ligand complexes. Finally, the chemical and biological spaces of the extensively tested, yet inactive, compounds were investigated to reconfirm their potential to be promising candidates.Die Polypharmakologie beschreibt einen Paradigmenwechsel von "einem Wirkstoff - ein Zielmolekül" zu "einem Wirkstoff - viele Zielmoleküle" und zeigt zugleich auf, dass hochwirksame Medikamente nur durch die Interaktion mit mehreren Zielmolekülen Ihre komplette Wirkung entfalten können. Hierbei ist die biologische Aktivität eines Medikamentes direkt mit deren Nebenwirkungen assoziiert, was durch die Interaktion mit therapeutischen bzw. Off-Targets erklärt werden kann (Promiskuität). Ein Ungleichgewicht dieser Wechselwirkungen resultiert oftmals in mangelnder Wirksamkeit, Toxizität oder einer ungünstigen Pharmakokinetik, anhand dessen man das Scheitern mehrerer potentieller Wirkstoffe in ihrer präklinischen und klinischen Entwicklungsphase aufzeigen kann. Die frühzeitige Vorhersage des pharmakologischen und toxikologischen Profils durch computergestützte Modelle (in-silico) anhand der chemischen Struktur kann helfen den Prozess der Medikamentenentwicklung zu verbessern. Eine Voraussetzung für die erfolgreiche Vorhersage stellen zuverlässige Bioaktivitätsdaten dar. Allerdings ist die Datenqualität oftmals ein zentrales Problem bei der Datenintegration. Die Ursache hierfür ist die Verwendung von verschiedenen Bioassays und „Readouts“, deren Daten zum Großteil aus primären und bestätigenden Bioassays gewonnen werden. Während ein Großteil der Treffer aus primären Assays als falsch-positiv eingestuft werden, zeigen einige Substanzen keine biologische Aktivität, obwohl sie in beiden Assay- Typen ausgiebig getestet wurden (“extensively assayed compounds”). In diese Arbeit wurden verschiedene chemoinformatische Methoden entwickelt und angewandt, um die zuvor genannten Probleme zu thematisieren sowie Lösungsansätze aufzuzeigen und im Endeffekt die Arzneimittelforschung zu beschleunigen. Hierfür wurden nicht redundante, Hand-validierte Wissensdatenbanken für zugelassene und zurückgezogene Medikamente erstellt und mit weiterführenden Informationen angereichert, um die Entdeckung und Optimierung kleiner organischer Moleküle voran zu treiben. Ein entscheidendes Tool ist hierbei die Aufklärung derer Wirkmechanismen sowie Off-Target-Interaktionen. Für die weiterführende Charakterisierung von Nebenwirkungen, wurde ein Hauptaugenmerk auf Nuklearrezeptoren, Pathways in welchen Stressrezeptoren involviert sind sowie den hERG-Kanal gelegt und mit in-silico Modellen simuliert. Die Erstellung dieser Modelle wurden Mithilfe eines integrativen Ansatzes aus “state-of-the-art” Algorithmen wie Ähnlichkeitsvergleiche und “Machine- Learning” umgesetzt. Um ein hohes Maß an Vorhersagequalität zu gewährleisten, wurde bei der Evaluierung der Datensätze explizit auf die Datenqualität und deren chemische Vielfalt geachtet. Weiterführend wurden die in-silico-Modelle dahingehend erweitert, das Substrukturfilter genauer betrachtet wurden, um richtige Wirkmechanismen von unspezifischen Bindungsverhalten (falsch- positive Substanzen) zu unterscheiden. Abschließend wurden der chemische und biologische Raum ausgiebig getesteter, jedoch inaktiver, kleiner organischer Moleküle (“extensively assayed compounds”) untersucht und mit aktuell zugelassenen Medikamenten verglichen, um ihr Potenzial als vielversprechende Kandidaten zu bestätigen