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The efficient interconversion of chemicals and electricity through electrocatalytic processes is central to many renewable-energy initiatives. The sluggish kinetics of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) 1 – 4 has long posed one of the biggest challenges in this field, and electrocatalysts based on expensive platinum-group metals are often required to improve the activity and durability of these reactions. The use of alloying 5 – 7 , surface strain 8 – 11 and optimized coordination environments 12 has resulted in platinum-based nanocrystals that enable very high ORR activities in acidic media; however, improving the activity of this reaction in alkaline environments remains challenging because of the difficulty in achieving optimized oxygen binding strength on platinum-group metals in the presence of hydroxide. Here we show that PdMo bimetallene—a palladium–molybdenum alloy in the form of a highly curved and sub-nanometre-thick metal nanosheet—is an efficient and stable electrocatalyst for the ORR and the OER in alkaline electrolytes, and shows promising performance as a cathode in Zn–air and Li–air batteries. The thin-sheet structure of PdMo bimetallene enables a large electrochemically active surface area (138.7 square metres per gram of palladium) as well as high atomic utilization, resulting in a mass activity towards the ORR of 16.37 amperes per milligram of palladium at 0.9 volts versus the reversible hydrogen electrode in alkaline electrolytes. This mass activity is 78 times and 327 times higher than those of commercial Pt/C and Pd/C catalysts, respectively, and shows little decay after 30,000 potential cycles. Density functional theory calculations reveal that the alloying effect, the strain effect due to the curved geometry, and the quantum size effect due to the thinness of the sheets tune the electronic structure of the system for optimized oxygen binding. Given the properties and the structure–activity relationships of PdMo metallene, we suggest that other metallene materials could show great promise in energy electrocatalysis. PdMo bimetallene, a highly curved and sub-nanometre-thick nanosheet of a palladium–molybdenum alloy, is an efficient and stable electrocatalyst for the oxygen reduction and evolution reactions under alkaline conditions.

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

PurposeThis study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain.MethodsWe retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families.ResultsInternal validation of the selected models returned Q2 values close to 0.60. External validation also gave r2 values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r2 0.74 in external validation.ConclusionsPerformance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule.

The ability to determine which environmental chemicals pose the greatest potential threats to human health remains one of the major concerns in regulatory toxicology. Computation methods that can accurately predict the chemicals’ toxic potential in silico are increasingly sought-after to replace in vitro high-throughput screening (HTS) as well as controversial and costly in vivo animal studies. To this end, we have built Quantitative Structure-Activity Relationship (QSAR) models of twelve (12) stress response and nuclear receptor signaling pathways toxicity assays as part of the 2014 Tox21 Challenge. Our models were built using the Random Forest, Deep Neural Networks and various combinations of descriptors and balancing protocols. All of our models were statistically significant for each of the 12 assays with the balanced accuracy in the range between 0.58 and 0.82. Our results also show that models built with Deep Neural Networks had high accuracy than those developed with simple machine learning algorithms and that dataset balancing led to a significant accuracy decrease.

Background The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion. Results By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae , we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined. Conclusion The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.