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Mycochemicals in wild and cultivated mushrooms: nutrition and health
The mushrooms have contributed to the development of active ingredients of fundamental importance in the field of pharmaceutical chemistry as well as of important tools in human and animal health, nutrition, and functional food. This review considers studies on the beneficial effects of medicinal mushrooms on the nutrition and health of humans and farm animals. An overview of the chemical structure and composition of mycochemicals is presented in this review with particular reference to phenolic compounds, triterpenoids and sterols, fatty acids and lipids, polysaccharides, proteins, peptides, and lectins. The nutritional value and chemical composition of wild and cultivated mushrooms in Italy is also the subject of this review which also deals with mushrooms as nutraceuticals and the use of mushrooms in functional foods. The nutraceutical benefits of UV irradiation of cultivated species of basidiomycetes to generate high amounts of vitamin D2 is also highlighted and the ability of the muhsrooms to inhibit glycation is analyzed. Finally, attention is paid to studies on bioactivities of some Italian wild and cultivated mushrooms with particular reference to species belonging to the genus
Pleurotus
. The review highlights the potential of medicinal mushrooms in the production of mycochemicals that represent a source of drugs, nutraceutical, and functional food.
Graphic abstract
Journal Article
A review of optical chemical structure recognition tools
Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of the molecules. With a backlog of decades of chemical literature in printed form not properly represented in open-access databases, there is a high demand for the translation of graphical molecular depictions into machine-readable formats. This translation process is known as Optical Chemical Structure Recognition (OCSR). Today, we are looking back on nearly three decades of development in this demanding research field. Most OCSR methods follow a rule-based approach where the key step of vectorization of the depiction is followed by the interpretation of vectors and nodes as bonds and atoms. Opposed to that, some of the latest approaches are based on deep neural networks (DNN). This review provides an overview of all methods and tools that have been published in the field of OCSR. Additionally, a small benchmark study was performed with the available open-source OCSR tools in order to examine their performance.
Journal Article
Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
Scientific contribution
The new DECIMER model presented here refines our previous research efforts and is currently the only open-source model tailored specifically for the recognition of hand-drawn chemical structures. The enhanced model performs better in handling variations in handwriting styles, line thicknesses, and background noise, making it suitable for real-world applications. The DECIMER hand-drawn structure recognition model and its source code have been made available as an open-source package under a permissive license.
Graphical Abstract
Journal Article
Models of necessity
The way chemists represent chemical structures as two-dimensional sketches made up of atoms and bonds, simplifying the complex three-dimensional molecules comprising nuclei and electrons of the quantum mechanical description, is the everyday language of chemistry. This language uses models, particularly of bonding, that are not contained in the quantum mechanical description of chemical systems, but has been used to derive machine-readable formats for storing and manipulating chemical structures in digital computers. This language is fuzzy and varies from chemist to chemist but has been astonishingly successful and perhaps contributes with its fuzziness to the success of chemistry. It is this creative imagination of chemical structures that has been fundamental to the cognition of chemistry and has allowed thought experiments to take place. Within the everyday language, the model nature of these concepts is not always clear to practicing chemists, so that controversial discussions about the merits of alternative models often arise. However, the extensive use of artificial intelligence (AI) and machine learning (ML) in chemistry, with the aim of being able to make reliable predictions, will require that these models be extended to cover all relevant properties and characteristics of chemical systems. This, in turn, imposes conditions such as completeness, compactness, computational efficiency and non-redundancy on the extensions to the almost universal Lewis and VSEPR bonding models. Thus, AI and ML are likely to be important in rationalizing, extending and standardizing chemical bonding models. This will not affect the everyday language of chemistry but may help to understand the unique basis of chemical language.
Journal Article
Classical Methods in Structure Elucidation of Natural Products
The structures of many natural products are given in standard textbooks on organic chemistry as 'established facts'. Yet for those natural products whose structures were determined between 1860 and 1960 by classical chemical methods, the lines of evidence are frequently buried under any number of investigations that led to dead ends and to revised structure assignments. Since very little is known about the structure clarification of these products at present, this volume serves to shed light once again on the achievements of previous generations of chemists, who worked with minimal experimental tools.
The selection of the 25 representative examples is subjective and arbitrary, dictated by the author's pleasure in recovering fundamental milestones in organic chemistry, with each chapter devoted to one organic compound. The time period covered, however, is more precisely defined: 1860 represents the advent of structure theory, prior to which there was no conceptual framework to address the 'structure' of a compound. One hundred years later, 1960 approximately marks the change from classical structure elucidation to the era in which structure elucidation is mainly based on spectroscopic evidence and X-ray crystallography. Since the emphasis of this work is on classical structure elucidation, work performed later than 1960 is only considered in exceptional cases.
Rather than simply provide a history of structure elucidation of particular natural products, the author combines results from historic experiments to trace a line of evidence for those structures that are nowadays accepted as established. This line of evidence may follow the path put forward by the original contributors, yet in some cases the experimental facts have been combined to form another, hopefully shorter, line of evidence. As a result, readers are able to ascertain for themselves the 'facts behind the established structure assignments' of a number of important natural products.
eBook
An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor
Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent neural networks (RNNs) have been shown to be an effective method to generate novel chemical structures in the form of SMILES. However, ligands generated by current methods have so far provided relatively low diversity and do not fully cover the whole chemical space occupied by known ligands. Here, we propose a new method (DrugEx) to discover de novo drug-like molecules. DrugEx is an RNN model (generator) trained through reinforcement learning which was integrated with a special exploration strategy. As a case study we applied our method to design ligands against the adenosine A
2A
receptor. From ChEMBL data, a machine learning model (predictor) was created to predict whether generated molecules are active or not. Based on this predictor as the reward function, the generator was trained by reinforcement learning without any further data. We then compared the performance of our method with two previously published methods, REINVENT and ORGANIC. We found that candidate molecules our model designed, and predicted to be active, had a larger chemical diversity and better covered the chemical space of known ligands compared to the state-of-the-art.
Journal Article
The Effect of High Molecular Weight Bio-based Diamine Derivative of Dimerized Fatty Acids Obtained from Vegetable Oils on the Structure, Morphology and Selected Properties of Poly(ether-urethane-urea)s
In this work, the effect of the high molecular weight bio-based diamine on the chemical structure and selected properties of poly(ether-urethane-urea)s has been investigated. The ether-urethane prepolymer was cured using 1,4-butanediol and/or bio-based diamine. Mentioned chain extenders were used separately or in the mixture, and their different molecular weight and chemical structure resulted in obtaining materials with diversified mechanical performence. The presence of specific chemical groups (i.e. urethane and urea groups) was confirmed by FTIR method. For the synthesized poly(ether-urethane-urea)s morphology and fracture mechanism, thermo-mechanical properties and mechanical properties were determined and discussed. Results confirmed that bio-based diamine acts as soft segments, and this is connected with changing of mechanical and thermo-mechanical properties of prepared partially bio-based poly(ether-urethane-urea)s. The increasing content of bio-based diamine resulted in increasing of tensile modulus and decreasing of tensile strength and elongation at break, and this is resulted from chemical structure of bio-based diamine (i.e. presence of aliphatic side chains).
Journal Article
Litter chemical structure is more important than species richness in affecting soil carbon and nitrogen dynamics including gas emissions from an alpine soil
Plant litter can influence many fundamental ecosystem functions during decomposition. However, the mechanism of litter diversity effects on belowground ecological processes remains unclear, especially with regard to soil C and the N cycle in alpine ecosystems. In this study, we incubated the litter of four alpine steppe species (SP: Stipa purpurea, CM: Carex moorcroftii, LP: Leontopodium pusillum, AN: Artemisia nanschanica) alone or in mixture with soil. The litter-mixing experiment was conducted to determine the effects of litter diversity on soil C and N dynamics in an alpine steppe in Northern Tibet. Litter treatments significantly enhanced CO₂ and N₂O emissions and decreased CH₄ immobilization in general; soil organic C, total N, water soluble organic C, water soluble organic N, microbial biomass C, microbial biomass N, and urease activity were also enhanced, while soil total inorganic N was decreased by litter treatments. Plant species richness poorly affected soil C and N dynamics, while litter chemical structure, such as C, N, lingin:N, phenol:N, cellulose, and cellulose:N, significantly affected soil C and N dynamics. Non-additive effects of litter mixture were predominant on soil C and N dynamics, while antagonistic effects were more frequent than synergistic effects. These results indicated that litter addition can significantly impact soil C and N dynamics through non-additive effects of litter mixture, and litter chemical structure is more important than species richness in affecting soil C and N dynamics of the alpine steppe in Northern Tibet.
Journal Article
Chemical Structure of Organic Matter in Water-Stable Macroaggregates of Agrochernozems of Different Positions on the Slope
The chemical structure of organic matter (OM) pools in the 2–1-mm water-stable macroaggregates isolated from air-dry aggregates of the same size in arable horizons of noneroded, eroded, and depositional agrochernozems was studied with solid-state
13
C-NMR spectroscopy. The changes in their chemical structure in the denudation–accumulative landscape are assessed. The overwhelming majority of water-stable macroaggregates in the erosional zone are newly formed due to dynamic replacement of OM in situ, which is clearly demonstrated by the integral chemical structure indicators in all OM pools in macroaggregates. Analytical data suggest the prevalent transport of newly formed macroaggregates. The destruction of macroaggregates during the transport phase is accompanied by the release of previously physically protected aggregated OM, which undergoes partial mineralization. Note that its most labile (hydrolyzable) part is predominately mineralized, whereas its stable part remains weakly changed or intact. Mineral-associated OM (clay and residue) weakly changes or does not change at all, retaining relative freshness, which indirectly suggests the prevalent migration of newly formed macroaggregates from the erosional zone. A greater degree of freshness of LF
fr
(free OM) in macroaggregates of depositional agrochernozem results from the abundance of fresh crop residues of the depositional zone together with the residues transported from the erosional zone.
Journal Article