<jats:p>The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.</jats:p>

AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Oxidovanadium complexes are an important class of homogeneous catalysts with paramount importance for the synthesis of valuable fine and bulk chemicals and chemical intermediates. However, their low chemical-thermal stability and difficult separation from the reaction medium hamper their implementation in industrial processes. In this sense, the quest for recyclable and eco-sustainable supported vanadium-based catalytic systems has been a longstanding goal. The aim of this chapter is to provide an overview of the widespread potential of supported vanadium complexes and other vanadium-containing solid-state compounds as eco-sustainable recyclable catalysts on several important reactions for the production of high value-added products. The progress on the development of efficient and reusable oxidovanadium catalysts immobilized onto different types of bulk and nano supports, or integrated on organic framework structures, will be reviewed. Special emphasis will be given to literature reports in which the catalytic performance of the vanadium-based heterogeneous systems surpasses that of the corresponding homogeneous counterparts. The application of vanadium-based materials as electrocatalysts for reduction-oxidation reactions relevant for renewable energy storage and conversion technologies will be also described. Subsequently, vanadium-mediated catalytic reactions for biomass valorization will be addressed. Finally, some insights on the latest theoretical findings on supported vanadium catalysts will be provided. © Royal Society of Chemistry 2021.

<jats:p>Creating a fun, interactive, and useful science activity for teaching purposes can be a real challenge, especially if it is addressed to middle-school children. More and more science communicators are employing novel communication techniques to better reach out to their audience. In science communication, storytelling is valuable to sparking interest in science. Given that there are many episodes in the history of science that can serve as inspiration, the authors of this chapter share how they used storytelling, based on a real-life event, to create a science communication activity for middle-school children. Focused on chemistry and ethics, these topics were introduced through hands-on laboratory activities with ethical questions embedded in the story line. This task challenges the students to come up with answers by themselves, through a problem-based learning model. By adding game logic elements to this activity, the authors created a unique form of communicating science, both educational and entertaining, which children appreciated. </jats:p>

In recent years, estimation of the aqueous toxicity of hazardous chemical substances has driven much of regulatory actions toward environmental pollutants, and chemometric modeling has already become a new standard for this. Daphnia is one of the most commonly used species for testing the aquatic toxicity. This chapter portrays the recent advances of chemometric modeling of daphnia toxicity of various categorized and non-categorized chemical substances. In this context, quantitative structure-toxicity relationship (QSTR) and interspecies quantitative (structure) toxicity-toxicity relationship (QTTR/QSTTR) studies reported in the last 10 years (2010-2020) are discussed in a chronological order. Emphasis is given to the descriptor calculation methods, model development strategies, model validation techniques, and overall outcomes of models in terms of predictability and mechanistic interpretation. Attention is particularly paid to the latest trends and the challenges of chemometric modeling of environment toxicity of chemicals. Finally, future opportunities of such modeling are also discussed at the end of the chapter. © 2022 John Wiley and Sons, Inc.

<jats:p>All living things are related to one another [...]</jats:p>

Biofilms play an important role in health, being associated with >80% of all microbial infections in the body and in the development of antibiotic resistance. Research in this field has continuously produced large volumes of data. Being able to handle all this information will be paramount for progress in this field. However, this places a heavy burden on the development of strategies to gather, organize and make this information available in a way that can be readily and effectively used by those requiring it. Lately, efforts towards this goal have been reported, particularly with the development of Quorumpeps, BiofOmics, BaAMPs, QSPpred, dPABBs, aBiofilm and the Biofilms Structural Database. This work reviews these databases and highlights their applicability and potential, while stressing some of the challenges for the coming years in database development and usage brought about by the use of big data and machine learning.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES-and because the vast majority of DES has yet to be synthesized-the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.</p>