Mutual support of ligand- and structure-based approaches : to what extent we can optimize the power of predictive model? : case study of opioid receptors

The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligandand structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligandand structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of Ki values) and regression (where exact Ki value was predicted) experiments

The influence of negative training set size on machine learning-based virtual screening

BACKGROUND: The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. RESULTS: The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. CONCLUSIONS: In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening

The significance of halogen bonding in ligand-receptor interactions : the lesson learned from molecular dynamic simulations of the D4D_{4} receptor

Recently, a computational approach combining a structure–activity relationship library containing pairs of halogenated ligands and their corresponding unsubstituted ligands (called XSAR) with QM-based molecular docking and binding free energy calculations was developed and used to search for amino acids frequently targeted by halogen bonding, also known as XB hot spots. However, the analysis of ligand–receptor complexes with halogen bonds obtained by molecular docking provides a limited ability to study the role and significance of halogen bonding in biological systems. Thus, a set of molecular dynamics simulations for the dopamine D4 receptor, recently crystallized with the antipsychotic drug nemonapride (5WIU), and the five XSAR sets were performed to verify the identified hot spots for halogen bonding, in other words, primary (V5x40), and secondary (S5x43, S5x461 and H6x55). The simulations confirmed the key role of halogen bonding with V5x40 and H6x55 and supported S5x43 and S5x461. The results showed that steric restrictions and the topology of the molecular core have a crucial impact on the stabilization of the ligand–receptor complex by halogen bonding

Losy rodzin katyńskich w pierwszym okresie po tzw. wyzwoleniu. Demitologizacja aksjologicznego przekonania historyków i członków rodzin katyńskich

The article presents selected representative examples of the fate of Katyn families in the first period after the war – the period of building and strengthening the communist regime. The axiological image of the fate of the murdered POWs and prisoners' s families was compared with the actual causes of repression experienced by Poles: repression against soldiers and activists of the independence resistance, people supporting the resistance base, or other from repressed social classes. Contrary to the belief of historians to date, in the late 1940s and early 1950s – in the period when people were most often sentenced to criminal sanctions for proclaiming the truth about the Katyn massacre – family members of the victims of the Katyn massacre constituted a small percentage of those repressed.W artykule przedstawiono wybrane, reprezentatywne przykłady losów rodzin katyńskich w pierwszym okresie powojennym, będącym okresem budowania i umacniania reżimu komunistycznego. Zestawiono aksjologiczne wyobrażenie o losach rodzin jeńców i więźniów zamordowanych na mocy decyzji KC WKP(b) z rzeczywistymi przyczynami represji, jakich wówczas na masową skalę doświadczali zaangażowani w działalność niepodległościową w okresie okupacji i po wojnie, tzw. obcych klasowo. Wbrew dotychczasowemu przekonaniu historyków, iż pod koniec lat czterdziestych i na początku lat pięćdziesiątych, a więc w okresie, kiedy najczęściej skazywano ludzi na różnego rodzaju sankcje karne za głośne wypowiedzi na temat rzeczywistego charakteru sowieckiego mordu, członkowie rodzin ofiar zbrodni katyńskiej stanowili niewielki odsetek wśród represjonowanych, nawet biorąc pod uwagę, że rzeczywista skala represji była większa niż ma to odzwierciedlenie w źródłach wytworzonych przez organa represji

ASSOCIATION BETWEEN AFFECTIVE DISORDERS AND THE USAGE OF LOW AND MEDIUM DOSES OF CORTICOSTEROIDS

It is well known that high doses of corticosteroids can induce psychiatric disorders, especially the maniacal states. However there are only a few studies that investigate the effects of low and medium doses of such drugs used for a long period of time on the patient’s mental health. The aim of the study was to investigate the prevalence and severity of affective disorders among the patients using up to 40 mg of corticosteroids (prednisone or equivalent). 54 patients during the corticosteroid therapy and 51 healthy controls were assessed with the use of the following questionnaires: Hypomania Checklist 16 (HCL-16), Beck Depression Inventory (BDI) and authors’ questionnaire. The results were analysed using SPSS Statistics version 12.5. The results indicated statistically significant differences in the prevalence of hypomania and depression between controls and study group. There were no evident correlations between either the dose or the length of the therapy and the severity of affective disorders. The study has shown that the usage of corticosteroids doses below 40 mg does have an effect on the prevalence of affective disorders. The occurrence of hypomania and depression was more common among the study group. To assess correlations further studies are needed

Mixed adenoneuroendocrine carcinoma: case report and review of literature

  Mixed adenoneuroendocrine carcinoma (MANEC) is a rare malignant neoplasm of a complex pathomorphological pattern combining the features of adenocarcinoma with a neuroendocrine component. According to the new classification of the World Health Organization (WHO) from 2010, the nomenclature of neuroendocrine neoplasms (NEN) was changed using this name for the entire group of neoplasms. The name ‘neuroendocrine tumours’ covers highly diversified neoplasms, determined in the pathomorphological comparison as G1 (NET G1) or G2 (NET G2). In addition, neuroendocrine carcinomas (NEC) and mixed adenoneuroendocrine carcinomas (MANEC) are differentiated. In a search of the the PubMed database, approximately 50 reports about this carcinoma were found, mainly with the location in the stomach, lymph nodes, intestines, liver, peritoneum, gallbladder, pancreas, oesophagus. We have not found a description of the metastatic lesions typical of MANEC in the meninges, brainstem, and lungs. We present the case of a 63-year-old patient who was admitted to the Department of Neurology due to increased dizzi­ness with accompanying diplopia, headache, nausea, and numbness of the hands. These symptoms appeared one day prior to admission, with intensification at night. The patient was initially diagnosed at the Department of Laryngology due to deafness of the right ear and deep hearing loss in the left ear four weeks prior to admission to our department. In the neurological examination, the following findings were detected: conscious, anxious, dysarthric speech, insignificant inspiratory dyspnoea, deafness of the right ear, deep hearing loss in the left ear, pharyngeal and palatal reaction, weak tension of the palato-pharyngeal fold, bilateral signs of central damage of the seventh nerve, insignificant deviation of the tongue to the left, muscle tone of the limbs without deficit, insignificantly decreased muscle tone in the left limbs, ataxia in the lower limbs, bilateral plantar reflex. In the admissions ward, CT examination of the head was performed and a hypodense focus in the left cerebral hemisphere, hypodense foci around the frontal horn of the lateral ventricles and small malacia cavities at the level of the subcortical nuclei were detected. A lumbar puncture was performed obtaining fluid with increased cytosis and a decreased glucose level. On the second day of hospitalisation, the general and neurological condition of the patient significantly worsened and a ‘sympathetic storming’ appeared followed by circulatory arrest. The patient was efficiently resuscitated, but then there was another sudden cardiac arrest and despite a long period of CPR, cardiac and respiratory action was not restored. After pathomorphological examination it was established that the whole image corresponded to MANEC located in the stomach, with dissemination mainly to the meninges, brainstem and lungs. In differential diagnosis rare causes of diseases must always be taken into consideration. An additional difficulty in diagnosing MANEC is a difference in the names depending on literature and country of the origin of the article, as well as the lack of Polish equivalents of some names of neoplasms. Only a reliable histopathological analysis is able to detect neoplasms from this group

The influence of the inactives subset generation on the performance of machine learning methods

Background: A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors. Results: In this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance. Conclusions: The level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening

Marta Spranzi, The Art of Dialectic between Dialogue and Rhetoric. The Aristotelian Tradition, 2011

OmówienieMarta Spranzi, The Art of Dialectic between Dialogue and Rhetoric. The Aristotelian Tradition, Amsterdam-Philadelphia, John Benjamins Publishing Company  201