Two-dimensional simulation of quantum reflection

A propagation method for the scattering of a quantum wave packet from a potential surface is presented. It is used to model the quantum reflection of single atoms from a corrugated (metallic) surface. Our numerical procedure works well in two spatial dimensions requiring only reasonable amounts of memory and computing time. The effects of the surface corrugation on the reflectivity are investigated via simulations with a paradigm potential. These indicate that our approach should allow for future tests of realistic, effective potentials obtained from theory in a quantitative comparison to experimental data

A Binary Ant Colony Optimization Classifier for Molecular Activities

Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a Plasmodium falciparum inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information

Differential effects of L-tryptophan and L-leucine administration on brain resting state functional networks and plasma hormone levels

Depending on their protein content, single meals can rapidly influence the uptake of amino acids into the brain and thereby modify brain functions. The current study investigates the effects of two different amino acids on the human gut-brain system, using a multimodal approach, integrating physiological and neuroimaging data. In a randomized, placebo-controlled trial, L-tryptophan, L-leucine, glucose and water were administered directly into the gut of 20 healthy subjects. Functional MRI (fMRI) in a resting state paradigm (RS), combined with the assessment of insulin and glucose blood concentration, was performed before and after treatment. Independent component analysis with dual regression technique was applied to RS-fMRI data. Results were corrected for multiple comparisons. In comparison to glucose and water, L-tryptophan consistently modifies the connectivity of the cingulate cortex in the default mode network, of the insula in the saliency network and of the sensory cortex in the somatosensory network. L-leucine has lesser effects on these functional networks. L-tryptophan and L-leucine also modified plasma insulin concentration. Finally, significant correlations were found between brain modifications after L-tryptophan administration and insulin plasma levels. This study shows that acute L-tryptophan and L-leucine intake directly influence the brain networks underpinning the food-reward system and appetite regulation

Combinatorial QSAR Modeling of human Intestinal Absorption

Intestinal drug absorption in humans is a central topic in drug discovery. In this study, we use a broad selection of machine learning and statistical methods for the classification and numerical prediction of this key endpoint. Our dataset is based on a selection of 458 small drug-like compounds with FDA approval. Using easily available tools, we calculated one- to three-dimensional physicochemical descriptors and used various methods of feature selection (best-first backward selection, correlation analysis, and decision tree analysis). We then used decision tree induction (DTI), fragment-based lazy-learning (LAZAR), support vector machine classification, multilayer perceptrons, random forests, k-nearest neighbor and Naïve Bayes analysis to model absorption ratios and binary classification (well absorbed and poorly absorbed compounds). Best performance for classification was seen with DTI using the chi-squared analysis interaction detector (CHAID) algorithm yielding corrected classification rate of 88%, (Matthews correlation coefficient of 75%). In numeric predictions, the multilayer perceptron performed best achieving root mean squared error of 25.823 and a correlation coefficient of 0.6. In line with current understanding is the importance of descriptors such as lipophilic partition coefficients (logP) and hydrogen bonding. However, we are able to highlight the utility of gravitational indices and moments of inertia, reflecting the role of structural symmetry in oral absorption. Our models are based on s diverse dataset of marketed drugs representing a broad chemical space. These models therefore contribute substantially to the molecular understanding of human intestinal drug absorption and qualify for a generalized use in drug discovery and lead optimization

Pre-processing feature selection for improved C&RT models for oral absorption

There are currently thousands of molecular descriptors that can be calculated to represent a chemical compound. Utilizing all molecular descriptors in Quantitative Structure–Activity Relationships (QSAR) modeling can result in overfitting, decreased interpretability, and thus reduced model performance. Feature selection methods can overcome some of these problems by drastically reducing the number of molecular descriptors and selecting the molecular descriptors relevant to the property being predicted. In particular, decision trees such as C&RT, although they have an embedded feature selection algorithm, can be inadequate since further down the tree there are fewer compounds available for descriptor selection, and therefore descriptors may be selected which are not optimal. In this work we compare two broad approaches for feature selection: (1) a “two-stage” feature selection procedure, where a pre-processing feature selection method selects a subset of descriptors, and then classification and regression trees (C&RT) selects descriptors from this subset to build a decision tree; (2) a “one-stage” approach where C&RT is used as the only feature selection technique. These methods were applied in order to improve prediction accuracy of QSAR models for oral absorption. Additionally, this work utilizes misclassification costs in model building to overcome the problem of the biased oral absorption data sets with more highly absorbed than poorly absorbed compounds. In most cases the two-stage feature selection with pre-processing approach had higher model accuracy compared with the one-stage approach. Using the top 20 molecular descriptors from the random forest predictor importance method gave the most accurate C&RT classification model. The molecular descriptors selected by the five filter feature selection methods have been compared in relation to oral absorption. In conclusion, the use of filter pre-processing feature selection methods and misclassification costs produce models with better interpretability and predictability for the prediction of oral absorption