Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

Chemotherapy is commonly associated with limited effectiveness and unwanted side effects in normal cells and tissues, due to the lack of specificity of therapeutic agents to cancer cells when systemically administered. In brain tumors, the existence of both physiological barriers that protect tumor cells and complex resistance mechanisms to anticancer drugs are additional obstacles that hamper a successful course of chemotherapy, thus resulting in high treatment failure rates. Several potential surrogate therapies have been developed so far. In this context, hydrogel-based systems incorporating nanostructured drug delivery systems (DDS) and hydrogel nanoparticles, also denoted nanogels, have arisen as a more effective and safer strategy than conventional chemotherapeutic regimens. The former, as a local delivery approach, have the ability to confine the release of anticancer drugs near tumor cells over a long period of time, without compromising healthy cells and tissues. Yet, the latter may be systemically administered and provide both loading and targeting properties in their own framework, thus identifying and efficiently killing tumor cells. Overall, this review focuses on the application of hydrogel matrices containing nanostructured DDS and hydrogel nanoparticles as potential and promising strategies for the treatment and diagnosis of glioblastoma and other types of brain cancer. Some aspects pertaining to computational studies are finally addressed.

Chemical pollution of water has raised great concerns among citizens, lawmakers, and nearly all manufacturing industries. As the legislation addressing liquid effluents becomes more stringent, water companies are increasingly scrutinized for their environmental performance. In this context, emergent contaminants represent a major challenge, and the remediation of water bodies and wastewater demands alternative sorbent materials. One of the most promising adsorbing materials for micropolluted water environments involves cyclodextrin (CD) polymers and cyclodextrin-containing polysaccharides. Although cyclodextrins are water-soluble and, thus, unusable as adsorbents in aqueous media, they can be feasibly polymerized by using different crosslinkers such as epichlorohydrin, polycarboxylic acids, and glutaraldehyde. Likewise, with those coupling agents or after substituting hydroxyl groups with more reactive moieties, cyclodextrin units can be covalently attached to a pre-existing polysaccharide. In this direction, the functionalization of chitosan, cellulose, carboxymethyl cellulose, and other carbohydrate polymers with CDs is vastly found in the literature. For the system containing CDs to be used for remediation purposes, there are benefits from a synergy that arises from (i) the ability of CD units to interact selectively with a broad spectrum of molecules, forming inclusion complexes and higher-order supramolecular assemblies, (ii) the functional groups of the crosslinker comonomers, (iii) the three-dimensional structure of the crosslinked network, and/or (iv) the intrinsic characteristics of the polysaccharide backbone. In view of the most recent contributions regarding CD-based copolymers and CD-containing polysaccharides, this review discusses their performance as adsorbents in micropolluted water environments, as well as their interaction patterns, addressing the influence of their structural and physicochemical properties and their functionalization.

Cellulose and cyclodextrins possess unique properties that can be tailored, combined, and used in a considerable number of applications, including textiles, coatings, sensors, and drug delivery systems. Successfully structuring and applying cellulose and cyclodextrins conjugates requires a deep understanding of the relation between structural, and soft matter behavior, materials, energy, and function. This review focuses on the key advances in developing materials based on these conjugates. Relevant aspects regarding structural variations, methods of synthesis, processing and functionalization, and corresponding supramolecular properties are presented. The use of cellulose/cyclodextrin conjugates as intelligent platforms for applications in materials science and pharmaceutical technology is also outlined, focusing on drug delivery, textiles, and sensors.

A properly designed nanosystem aims to deliver an optimized concentration of the active pharmaceutical ingredient (API) at the site of action, resulting in a therapeutic response with reduced adverse effects. Due to the vast availability of lipids and surfactants, producing stable lipid dispersions is a double-edged sword: on the one hand, the versatility of composition allows for a refined design and tuning of properties; on the other hand, the complexity of the materials and their physical interactions often result in laborious and time-consuming pre-formulation studies. However, how can they be tailored, and which premises are required for a “right at first time” development? Here, a stepwise framework encompassing the sequential stages of nanoparticle production for disulfiram delivery is presented. Drug in lipid solubility analysis leads to the selection of the most suitable liquid lipids. As for the solid lipid, drug partitioning studies point out the lipids with increased capacity for solubilizing and entrapping disulfiram. The microscopical evaluation of the physical compatibility between liquid and solid lipids further indicates the most promising core compositions. The impact of the outer surfactant layer on the colloidal properties of the nanosystems is evaluated recurring to machine learning algorithms, in particular, hierarchical clustering, principal component analysis, and partial least squares regression. Overall, this work represents a comprehensive systematic approach to nanoparticle formulation studies that serves as a basis for selecting the most suitable excipients that comprise solid lipid nanoparticles and nanostructured lipid carriers.