Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models

In recent years, financial regulations such as Basel II and Solvency II have highlighted the utility of credit risk assessments through internal rating systems, particularly for estimating the probability of default (PD) of credit exposures.     

Reductive Elimination or C−C Bond Activation with Model Ni,Pd, Pt Complexes? A High-Accuracy Comparative Computational Analysis of Reactivity

The development of Pd- and Ni-catalyzed reactions for C−C bond formation is one of the primary driving forces in modern organic synthesis and the fine chemical industry. However, understanding the role of conformational mobility in reaction mechanisms is a long-standing challenge. We highlight the effect of a multirotamer (multiconformer) system on the effective Gibbs free energy of activation in the key C−C coupling process and promote the use of a simplified version of multiconformer transition state theory that is straightforward to apply. Multivariate regression helped to quantitatively map the effect of coupled organic substituents (their structural and electronic parameters), as well as to determine the relative activity of metals. We provide computational evidence for solvent control of the equilibrium in RE/C−C-bond activation for some model complexes. We also demonstrate that Ni complexes, being unique in the catalysis of sp³-sp³ couplings, can be more challenging for machine learning and computational chemistry. The modeling was performed at an exceptionally high level, DLPNO-CCSD(T)/CBS//RIJCOSX-PBE0-D4/def2-TZVP. The Conclusions section contains an infographic summarizing the key findings related to the fields of cross-coupling catalysis, machine learning in catalysis, and computational chemistry.     

A new sequence based encoding for prediction of host–pathogen protein interactions

Pathogen–host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen–host interactions. Developing a computational method to predict pathogen–host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen–host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen–host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.