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 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

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

Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design

Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least two decades in various fields of cheminformatics: from similarity searching to machine learning (ML). Advances in silico techniques consequently led to combining both these methodologies into a new approach known as pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., Estate, MACCS, PubChem, Substructure, Klekotha-Roth, CDK, Extended, and GraphOnly) and ChemAxon Pharmacophoric Features fingerprint. Pharmacoprint consisted of 39973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of bit string but also improved the efficiency of ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for 3D structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed to maximize Matthews Correlation Coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.Comment: Journal of Chemical Information and Modeling (2021

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

Fast and noninvasive hair test for preliminary diagnosis of mood disorders

The main objective of this study was to develop a test for the fast and noninvasive prediagnosis of mood disorders based on the noninvasive analysis of hair samples. The database included 75 control subjects (who were not diagnosed with depression) and 40 patients diagnosed with mood disorders such as depression or bipolar disorder. Both women and men, aged 18–65 years, participated in the research. After taking the hair samples, they were washed (methanol–water–methanol by shaking in a centrifuge for two min) and air-dried in a fume hood. Each hair collection was analyzed using Fourier transform infrared spectroscopy attenuated total reflection (ATR-FTIR) spectroscopy. Subsequently, the results obtained were analyzed based on chemometric methods: hierarchical cluster analysis (HCA) and principal component analysis (PCA). As a results of the research conducted, potential differences were noticed. There was a visible change in the spectra intensity at around 2800–3100 cm(−1) and smaller differences around 1460 cm(−1); the bands can be assigned to protein vibrations. However, these are preliminary studies that provide a good basis for the development of a test for the initial diagnosis of mood disorders

Structural determinants influencing halogen bonding : a case study on azinesulfonamide analogs of aripiprazole as 5-HT1A, 5-HT7, and D2 receptor ligands

This work was supported by Grant KNW-1-015/K/7/O from Medical University of Silesia, Katowice, Poland. Calculations have been carried out using resources provided by Wroclaw Centre for Networking and Supercomputing (http://wcss.pl), Grant No. 382.A series of azinesulfonamide derivatives of long-chain arylpiperazines with variable-length alkylene spacers between sulfonamide and 4-arylpiperazine moiety is designed, synthesized, and biologically evaluated. In vitro methods are used to determine their affinity for serotonin 5-HT1A, 5-HT6, 5-HT7, and dopamine D2 receptors. X-ray analysis, two-dimensional NMR conformational studies, and docking into the 5-HT1A and 5-HT7 receptor models are then conducted to investigate the conformational preferences of selected serotonin receptor ligands in different environments. The bent conformation of tetramethylene derivatives is found in a solid state, in dimethyl sulfoxide, and as a global energy minimum during conformational analysis in a simulated water environment. Furthermore, ligand geometry in top-scored complexes is also bent, with one torsion angle in the spacer (τ2) in synclinal conformation. Molecular docking studies indicate the role of halogen bonding in complexes of the most potent ligands and target receptors.[SU

Are the hydantoin-1,3,5-triazine 5−HT6R5-HT_{6}R ligands a hope to a find new procognitive and anti-obesity drug? : considerations based on primary in vivo assays and ADME-Tox profile in vitro

$5-HT_{6}R$ Though the $5-HT_{6}R$ serotonin receptor is an important target giving both agonists and antagonists similar therapeutic potency in the treatment of topic CNS-diseases, no $5-HT_{6}R$ ligand has reached the pharmaceutical market yet due to the too narrow chemical space of the known $5-HT_{6}R$ agents and insuffcient "drugability." Recently, a new group of non-indole and non-sulfone hydantoin-triazine $5-HT_{6}R$ ligands was found, where 3-((4-amino-6-(4-methylpiperazin-1-yl)- 1,3,5-triazin-2-yl)methyl)-5-methyl-5-(naphthalen-2-yl)imidazolidine-2,4-dione (KMP-10) was the most active member. This study is focused on wider pharmacological and "druglikeness" characteristics for KMP-10. A computer-aided insight into molecular interactions with $5-HT_{6}R$ has been performed. "Druglikeness" was examined using an eight-test panel in vitro, i.e., a parallel artificial membrane permeability assay (PAMPA), and Caco-2 permeability-, P-glycoprotein (Pgp) affnity-, plasma protein binding-, metabolic stability- and drug–drug interaction-assays, as well as mutagenicity- and HepG2-hepatotoxicity risk tests. Behavioral studies in vivo, i.e., elevated plus-maze (EPM) and novel object recognition (NOR) tests, were performed. Extended studies on the influence of KMP-10 on rats' metabolism, including biochemical tests, were conducted in vivo. Results indicated significant anxiolytic and precognitive properties, as well as some anti-obesity properties in vivo, and it was found to satisfy the "druglikeness" profile in vitro for KMP-10. The compound seems to be a good lead-structure and candidate for wider pharmacological studies in search for new CNS-drugs acting via $5-HT_{6}R$