QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.

An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Parvalbumin deficiency affects network properties resulting in increased susceptibility to epileptic seizures.

Networks of GABAergic interneurons are of utmost importance in generating and promoting synchronous activity and are involved in producing coherent oscillations. These neurons are characterized by their fast-spiking rate and by the expression of the Ca(2+)-binding protein parvalbumin (PV). Alteration of their inhibitory activity has been proposed as a major mechanism leading to epileptic seizures and thus the role of PV in maintaining the stability of neuronal networks was assessed in knockout (PV-/-) mice. Pentylenetetrazole induced generalized tonic-clonic seizures in all genotypes, but the severity of seizures was significantly greater in PV-/- than in PV+/+ animals. Extracellular single-unit activity recorded from over 1000 neurons in vivo in the temporal cortex revealed an increase of units firing regularly and a decrease of cells firing in bursts. In the hippocampus, PV deficiency facilitated the GABA(A)ergic current reversal induced by high-frequency stimulation, a mechanism implied in the generation of epileptic activity. We postulate that PV plays a key role in the regulation of local inhibitory effects exerted by GABAergic interneurons on pyramidal neurons. Through an increase in inhibition, the absence of PV facilitates synchronous activity in the cortex and facilitates hypersynchrony through the depolarizing action of GABA in the hippocampus.

Dopamine modulation of glutamate metabotropic receptors in conditioned reaction of sensory motor cortex neurons of the cat.

The dopamine modulation of microiontophoretic application of antagonists of glutamate metabotropic synaptic transmission was studied in the sensory motor cortex of awake cats during instrumental conditioned reflex. The substances depressed the background and intensity of evoked impulse activity of pyramidal neurons of the sensory-motor cortex and provoked significant increases in the latency of impulse reaction and corresponding conditioned movements of animals. Simultaneous application of the antagonists and dopamine (DA) eliminated their depression of the background and evoked activity of neurons and decreased the latency of the impulse reactions and movements. Similar qualitative effects were observed in experiments with simultaneous application of biccuculine and (RS)-alpha-methyl-4-carboxyphenylglycine. It is supposed that the DA modulation in the brain consists in stabilizing the background and evoked activity of cortical neurons during reduced intensity of metabotropic glutamatergic synaptic transmission. Such modulation can be important when considering some pathological disorders of glutamatergic transmission.

The effects of activation of glutamate ionotropic connections of neurons in the sensorimotor cortex in a conditioned reflex.

Changes in conditioned reflex spike activity of neurons in the sensorimotor cortex were studied during microiontophoretic application of agonists and antagonists of glutamate and GABAergic transmission. The results of these experiments showed that the glutamate ionotropic receptors (AMPA and NMDA) of neurons in the sensorimotor cortex were intensely activated by the arrival of a conditioned signal in the cortex. This response included not only large pyramidal neurons of the deep cortical layers, but also the surrounding inhibitory interneurons. The existence of constant tonic inhibitory regulation of the activity of large pyramidal neurons by the surrounding inhibitory cells was demonstrated, along with the active involvement of this inhibition in organizing the excitatory responses of neurons in the sensorimotor cortex during a conditioned reflex.

Dopamine modulation of activity of cat sensorimotor cortex neurons during conditioned reflexes.

The effects of iontophoretic application of dopamine and selective D1 or D2 dopamine receptor agonists and antagonists on impulse activity of neurons of the deep layers of the sensorimotor cortex of cat were investigated during performance of a conditioned paw movement task. The application of dopamine, Quinpirole (selective D2 receptor agonist) or SKF 38393 (selective D1 receptor agonist) increased both background (P<0.001) and evoked impulse activity (P<0.05 for selective agonists). Selective D2 and D1 receptor antagonists (Sulpiride and SKF 83566, respectively) both increased the latency of neural responses and significantly increased the latency of the conditioned paw movements (P<0.01). These data suggest that during natural physiological functions subcortical dopamine neurons provide facilitation of activity pyramidal neurons of sensorimotor cortex.

[Activation of glutamate ionotropic connections of sensorimotor cortex neurons during conditioning]. / Effekty aktivatsii glutamatnykh ionotropnykh sviazei neironov sensomotornoi kory pri uslovnom reflekse.

Changes in conditioned impulse reactions of neurons in sensorimotor cortex were studied during microiontophoretic application of glutamatergic and GABA ergic agonistic and antagonistic drugs. It was shown that ionotropic glutamate receptors (AMPA and NMDA) are activated by a conditioned stimulus. Not only large pyramidal neurons of deep cortical layers but surrounding short-axon inhibitory interneurons are involved in the reaction. It was shown that the activity of pyramidal neurons is under a constant inhibitory control from surrounding interneurons. This inhibition is involved in organization of excitatory cortical responses during conditioning.

Estimation of aqueous solubility of chemical compounds using E-state indices.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.

[Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds]. / Primenenie neironnykh setei, ispol'zuiushchikh algoritm prostranstvennogo obucheniia dlia issledovaniia sviazei struktura-aktivnost' khimicheskikh soedinenii.

A volume learning algorithm for artificial neural networks was developed to quantitatively describe the three-dimensional structure-activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped to reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.

Volume learning algorithm artificial neural networks for 3D QSAR studies.

The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies.

Different tonic regulation of neuronal activity in the rat dorsal raphe and medial prefrontal cortex via 5-HT(1A) receptors.

It has been established that 5-HT(1A) receptors are expressed both presynaptically as autoreceptors by 5-HT containing neurones, and postsynaptically by a variety of other neurones. Activation of either somatodendritic 5-HT(1A) autoreceptors or postsynaptic 5-HT(1A) receptors induces hyperpolarisation and inhibition of action potential discharge of the neurones, but it is unclear whether 5-HT(1A) receptors are under a general tonic influence by 5-HT. In the present study, using single unit recordings from both anesthetized and non-anesthetized rats, we show that the activity of neurones in the medial prefrontal cortex is not altered by systemic administration of the selective 5-HT(1A) receptor antagonist, WAY 100635. In contrast, WAY 100635 increased the firing rate of 5-HT neurones in the dorsal raphe nucleus. Our findings indicate a tonic activation of presynaptic somatodendritic but not postsynaptic cortical 5-HT(1A) receptors.

Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds.

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns.

The existence of precise temporal relations in sequences of spike intervals, referred to as 'spatiotemporal patterns', is suggested by brain theories that emphasize the role of temporal coding. Specific analytical methods able to assess the significance of such patterned activity are extremely important to establish its function for information processing in the brain. This study proposes a new method called 'pattern grouping algorithm' (PGA), designed to identify and evaluate the statistical significance of patterns which differ from each other by a defined and small jitter in spike timing of the order of few ms. The algorithm performs a pre-selection of template patterns with a fast computational approach, optimizes the jitter for each spike in the template and evaluates the statistical significance of the pattern group using three complementary statistical approaches. Simulated data sets characterized by various types of known non stationarities are used for validation of PGA and for comparison of its performance to other methods. Applications of PGA to experimental data sets of simultaneously recorded spike trains are described in a companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings. J Neurosci Method 2000; accompanying article).

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings.

This study demonstrates the practical application of the pattern grouping algorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 2000; accompanying article), to data sets including up to 30 simultaneously recorded spike trains. The analysis of a large network of simulated neurons shows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cortex was reversibly inactivated suggest that widespread cell assemblies contribute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal cortex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period preceding the cue was associated with changes in reaction time. In conclusion, neuronal network interactions may generate spatiotemporal firing patterns detectable by PGA. We provide evidence of such patterned activity associated with specific animal's behavior, thus suggesting the existence of complex temporal coding schemes in the higher nervous centers of the brain.

Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet.

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.

Non-linear cortico-cortical interactions modulated by cholinergic afferences from the rat basal forebrain.

In the adult rat most of basal forebrain cholinergic neurons (BFCN) express the low-affinity p75 nerve growth factor recceptor (NGFr). The immunotoxin 192 IgG-saporin (SAP) provokes a selective loss of NGFr-positive BFCN, somewhat similar to the loss of integrity of BFCN associated with human senile dementia of Alzheimer's type, whereas NGF exerts a trophic action on BFCN. Cortico-cortical interactions are modulated by cholinergic projections of BFCN and it is proposed that alterations of these projections by SAP and by NGF produce opposite effects. This hypothesis was tested by recording multiple local field potentials (LFPs) in the rat temporal cortex and applying bispectral analysis to measure phase-coupled frequencies, somewhat analogous to frequencies of resonance. Choline acetyltransferase (ChAT) activity was measured in the septal area in order to assess the effects of the treatments. NGF-treatment increased ChAT activity by 45% and frequencies of non-linear coupling were shifted towards frequencies higher than 70 Hz, thus suggesting the presence of increased functional interactions in the short range. By contrast, SAP provoked a decrease of nearly 40% in ChAT activity and an increase of phase-coupling in the low frequencies (< 50 Hz), being interpreted as a decreased functional cortico-cortical interaction. Bispectral analysis revealed features of the effect of BFCN on cortical activity that could not be observed by other means and offers as a valuable tool of study that could be extended to the EEG of Alzheimer's patients.

Pharmaceutical fingerprinting in phase space. 1. Construction of phase fingerprints.

The present study proposes a general method for constructing pharmaceutical fingerprints in the analysis of HPLC trace organic impurity patterns. The approach considers signals in phase space and accounts for two different types of noise: additive and perturbative. The first type, additive noise, contributes to distortion of the absolute values of signal peaks. The second type, perturbative noise, contributes to variations of the retention times of signal peaks and distorts the time scale of the trace organic impurity patterns. The ability of the proposed approach to consider both types of noise significantly distinguishes it from existing methods of data analysis that are usually designed to treat only the additive noise. Analysis of the HPLC signals in phase space eliminates the problem of perturbation noise and enables detection and comparison of similar signal segments recorded at different retention times. The current study analyzes the chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan using three HPLC columns. For five manufacturers the variability of data recorded with the same column are in perfect agreement with the proposed model. A significant variance of parameters is detected for one manufacturer, thus indicating a possible change in its product consistency. The analysis in phase space is also used to explain the previously detected variability of HPLC signals across columns. The accompanying paper reports an application of the proposed approach for the pattern recognition of HPLC data.

Pharmaceutical fingerprinting in phase space. 2. Pattern recognition.

The current study introduces an approach for pattern recognition of drug manufacturers according to their HPLC trace impurity data. This method considers signals in phase space and accounts for two different types of noise: additive and perturbative. The pharmaceutical fingerprints are estimated as mean trajectories of HPLC trace impurity data and are used as reference models for recognition of new data by the minimal length classifier. The chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan are analyzed as an example. The prediction ability of the new method tested using three different cross-validation procedures remains about 95% even if the number of available data in the training sets decreases by 5 times. The accuracy of prediction in phase space is superior compared to results calculated using a Window Preprocessing method and artificial neural networks. The difference in performance between new and previous methods becomes more significant under particular conditions that are more adequate for practical application of the method. In addition, the current approach enables simple and comprehensive interpretation of the calculated results.

Corticofugal modulation of functional connectivity within the auditory thalamus of rat, guinea pig and cat revealed by cooling deactivation.

Microelectrode recordings were simultaneously performed at multiple sites in the medial geniculate body (MGB) of anesthetized cats, rats and guinea pigs. We studied the effect of cortical deactivation on the association of neural activity within the thalamus during spontaneous activity. The corticofugal influence was suppressed by temporary cooling of the auditory cortex. Pairs of spike trains recorded from the same electrode were distinguished from cases where units were in MGB but recorded with different electrodes. Time domain analyses included crosscorrelations and search for precise repetition of complex spatiotemporal firing patterns of reverberating thalamic circuits. As a complementary approach we performed bispectral analyses of simultaneously recorded local field potentials in order to uncover the frequency components of their power spectra which are non linearly coupled. All results suggest that new functional neuronal circuits might appear at the thalamic level in the absence of input from the cortex. The newly active intrathalamic connections would provide the necessary input to sustain the reverberating activity of thalamic cell assemblies and generate low frequency non-linear interactions. The dynamic control exerted by the cortex over the functional segregation of information processing carried out in the thalamus conforms with theoretical neural network studies and with the functional selectivity-adaptive filtering theory of thalamic neuronal assemblies. Although this general conclusion remains valid across species, specific differences are discussed in the frame of known differences of the microcircuitry elements.

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task.

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.