Predictive Potential Of Models Research Articles

Brønsted Acid Catalysis - Controlling the Competition of Monomeric versus Dimeric Reaction Pathway Enhances Stereoselectivities.

Chiral phosphoric acids (CPA) have become a privileged catalyst type in organocatalysis, but the selection of the optimum catalyst is still challenging. So far hidden competing reaction pathways may limit the maximum stereoselectivities and the potential of prediction models. In CPA catalyzed transfer hydrogenation of imines, we identified for many systems two reaction pathways with inverse stereoselectivity, featuring as active catalyst either one CPA or a hydrogen bond bridged dimer. NMR measurements and DFT calculations revealed the dimeric intermediate and a stronger substrate activation via cooperativity. Both pathways are separable: Low temperatures and high catalysts loadings favor the dimeric pathway (ee up to -98%), while low temperatures with reduced catalyst loading favor the monomeric pathway and give significantly enhanced ee (92-99% ee; prior 68-86% at higher temperatures). Thus, a broad impact is expected on CPA catalysis regarding reaction optimization and prediction.

Brønstedsäure‐Katalyse – Kontrolle der Konkurrenz zwischen monomerem und dimerem Reaktionsweg erhöht Stereoselektivität

AbstractChirale Phosphorsäuren (CPS) sind inzwischen ein bevorzugter Katalysatortyp in der Organokatalyse, jedoch bleibt die Auswahl der optimalen Katalysatorstruktur weiterhin eine Herausforderung. Zusätzlich können unbekannte konkurrierende Reaktionswege die maximale Stereoselektivität und das Potenzial von Vorhersagemodellen einschränken. Bei der CPS‐katalysierten Transferhydrierung von Iminen haben wir für viele Systeme zwei Reaktionswege mit inverser Stereoselektivität gefunden, bei denen entweder eine monomere CPS oder ein Wasserstoffbrücken‐verknüpftes Dimer als Katalysator fungiert. NMR‐Messungen und DFT‐Berechnungen offenbarten ein dimeres Intermediat mit einer stärkeren Substrataktivierung durch Kooperativitätseffekte. Beide Wege können separiert werden: Niedrige Temperaturen und hohe Katalysatoranteile begünstigen den dimeren Reaktionsweg (ee bis zu −98 %), während niedrige Temperaturen und reduzierte Katalysatoranteil den monomeren Reaktionsweg fördern und zu einem signifikant verbesserten ee führen (92–99 % ee; vorher 68–86 % bei höheren Temperaturen). Insgesamt wird eine große Auswirkung auf die CPS‐Katalyse in Bezug auf Reaktionsoptimierung und Vorhersage erwartet.

The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity

Computational prediction of HIV-1 protease inhibitor via quantitative structure–activity relationships (QSARs) is a popular study in the field of computational chemistry. The aim of the present study was building up of QSAR models for anti-HIV activity by means of the CORAL software ( http://www.insilico.eu/coral ). Applying of correlation weights for five and six-member molecular rings as components of the target function in the Monte Carlo optimization that is aimed to build up correlation between activity of HIV-1 protease inhibitors expressed as pIC50 = lg[1/(IC50 × 109)] and optimal descriptor improves the predictive potential. Simplified molecular input-line entry system (SMILES) is used as the representation of the molecular structure of HIV-1 protease inhibitors for the QSAR analysis. New criterion of predictive potential so-called index of ideality of correlation (IIC) has been appllied to improve the model for HIV-1 protease inhibitors activity. High correlation coefficients were observed between the experimental and predicted anti-HIV activity. Applying of special ring code as component of the Monte Carlo calculation significantly improves the statistical quality of the model. Furthermore, applying of the IIC as component of the Monte Carlo optimization improves the predictive potential of the CORAL models. The presence of the rings and the quality of these are very important molecular features which are able to improve statistical quality of model for anti-HIV activity. In addition, the ability of IIC to improve predictive potential of a model has been confirmed.

Risk Prediction Using Routine Data: Development and Validation of Multivariable Models Predicting 30- and 90-day Mortality after Surgical Treatment of Colorectal Cancer

Quality control, benchmarking, and pay for performance (P4P) require valid indicators and statistical models allowing adjustment for differences in risk profiles of the patient populations of the respective institutions. Using hospital remuneration data for measuring quality and modelling patient risks has been criticized by clinicians. Here we explore the potential of prediction models for 30- and 90-day mortality after colorectal cancer surgery based on routine data. Full census of a major statutory health insurer. Surgical departments throughout the Federal Republic of Germany. 4283 and 4124 insurants with major surgery for treatment of colorectal cancer during 2013 and 2014, respectively. Age, sex, primary and secondary diagnoses as well as tumor locations as recorded in the hospital remuneration data according to §301 SGB V. 30- and 90-day mortality. Elixhauser comorbidities, Charlson conditions, and Charlson scores were generated from the ICD-10 diagnoses. Multivariable prediction models were developed using a penalized logistic regression approach (logistic ridge regression) in a derivation set (patients treated in 2013). Calibration and discrimination of the models were assessed in an internal validation sample (patients treated in 2014) using calibration curves, Brier scores, receiver operating characteristic curves (ROC curves) and the areas under the ROC curves (AUC). 30- and 90-day mortality rates in the learning-sample were 5.7 and 8.4%, respectively. The corresponding values in the validation sample were 5.9% and once more 8.4%. Models based on Elixhauser comorbidities exhibited the highest discriminatory power with AUC values of 0.804 (95% CI: 0.776 -0.832) and 0.805 (95% CI: 0.782-0.828) for 30- and 90-day mortality. The Brier scores for these models were 0.050 (95% CI: 0.044-0.056) and 0.067 (95% CI: 0.060-0.074) and similar to the models based on Charlson conditions. Regardless of the model, low predicted probabilities were well calibrated, while higher predicted values tended to be overestimates. The reasonable results regarding discrimination and calibration notwithstanding, models based on hospital remuneration data may not be helpful for P4P. Routine data do not offer information regarding a wide range of quality indicators more useful than mortality. As an alternative, models based on clinical registries may allow a wider, more valid perspective.

Spatial Modeling of Vegetation Potential: An Introduction

In the face of global change, the mapping of vegetation and its underlying site conditions has re-entered the focus of applied vegetation science. Thus, land managers, conservationists and scientists require a new generation of mapped and interpreted environmental information as a basis for scenario building, planning and decision making. Nowadays, such mapping is rarely performed by experts in the field, but rather by the synthesis of ecological information from point data and area-wide layers stored in geographical information systems (GIS). The prerequisites and main reasons to prefer these methods are (1) the availability of point data from vegetation plots and concomitant soil profiles, (2) availability of high resolution geo-physical data, and (3) the predictive potential of models that allow the application of climate change scenarios.