Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.

Quinoxaline derivatives are an important class of heterocycle compounds, where N replaces some carbon atoms in the ring of naphthalene. Its molecular formula is C8H6N2, formed by the fusion of two aromatic rings, benzene and pyrazine. It is rare in natural state, but their synthesis is easy to perform. In this review the State of the Art will be presented, which includes a summary of the progress made over the past years in the knowledge of the structure and mechanism of the quinoxaline and quinoxaline derivatives, associated medical and biomedical value as well as industrial value. Modifying quinoxaline structure it is possible to obtain a wide variety of biomedical applications, namely antimicrobial activities and chronic and metabolic diseases treatment.

Fullerenes are carbon allotropes, and they have called the attention of scientists in the last 15 years. In nanotechnology, fullerenes have had several promising applications in medicinal chemistry, pharmaceutical sciences, biomedicine, and related disciplines. Particularly, the design and biological evaluation of fullerene-C-60 derivatives as antimicrobial agents constitute essential components of several active areas of research that continue to grow. There is a serious concern due to the emergence of resistance of pathogens to current antibiotics, and consequently, the task of finding new and more efficient antimicrobial therapies is increasingly challenging. This review is devoted to discuss the most recent advances in the discovery of structures containing fullerene-C-60 as models of nanoentities-based antibacterial agents. In addition, by considering the role of the toxicity associated to the nanoparticles, we introduce a general multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). This model was created from a heterogeneous dataset containing more than 47200 statistical cases, and it was focused on performing simultaneous predictions of multiple ADMET (absorption, distribution, metabolism, elimination) properties. The mtk-QSBER model could correctly classify more than 90% of the cases in the whole database, being employed for virtual screening of diverse ADMET profiles of different molecular architectures containing fullerene-C-60. The theoretical results were in agreement with the experimental evidences, confirming that the increment in the number of polar regions associated to fullerene-C-60 can improve the safety profiles. At the same time, this fact demonstrated the ability of the present mtk-QSBER model to be used as an efficient tool for in silico assessment of different safety profiles of large libraries of compounds under dissimilar experimental conditions.

The iminium aza-Diels-Alder (iADA) reaction of cyclopentadiene with 16 protonated alkyl alkylimineglyoxilates was studied using density functional theory (DFT) in order to elucidate how different substitution patterns in the dienophile may affect the reaction's outcome. Additionally, the application of the polarisable continuum model (PCM) together with the evaluation of the thermodynamic properties at different temperatures further allowed the surveying of the importance of such factors. These effects were combined into linear models which use the temperature, Taft's constants and characteristics of the solvent as descriptors for modelling and predicting the activation enthalpy and enthalpic balance of the iADA reactions under study. This model performs in a satisfactory manner, providing a cross-validation of the DFT framework traditionally used for predicting the outcome of these reactions and also uncovering novel insights into how the substitution patterns in the dienophile, the solvent and the temperature interact in order to give the characteristic regio- and stereoselectivity of the iADA reaction. Moreover, the results show that Taft's polar and steric substituent constants are important descriptors for assessing the outcome of iADA reactions.

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems) The best models for these interactions have been implemented in two free Web tools.