Linking the unique molecular complexity of dissolved organic matter to flood period in the Yangtze River mainstream

Large rivers transport a significant amount of terrestrially derived dissolved organic matter (DOM) to coastal oceans, consisting of a critical component of the global biogeochemical cycle. Although high flow events usually introduce more terrestrial DOM than baseflow, the underlying molecular complexity and lability of DOM during high discharge are not well constrained, especially in large river ecosystems. By combining ultraviolet and fluorescent spectroscopy, and ultrahigh-resolution mass spectrometry, we found that stronger terrestrial DOM signal was detected during high discharge than normal discharge in the Yangtze River mainstream. The averaged DOC concentration was higher during high discharge than normal discharge. Optical properties confirmed higher aromaticity and relatively higher humic-like fluorescent components in DOM during high discharge. The molecular composition showed significantly higher molecular complexity, averaged molecular weight, aromaticity, relative abundances of polyphenols and highly unsaturated compounds of DOM during high discharge than normal discharge. A large set of unique molecular formulae (up to 4927) was only detected during high discharge. These unique molecular formulae were mostly lignin degradation products, likely due to more intensive soil leaching during high discharge. By comparing with incubation experiments and the Yangtze River mouth and East China Sea DOM molecular composition, some of these unique molecular formulae during high discharge are resistant to both bio- and photo-degradation, and persist during their transport to the East China Sea. Therefore, we suggest that high discharge will additionally introduce a relatively recalcitrant pool of DOM into the Yangtze River mainstream and persist during its journey to the ocean. Considering the projected increase of flood frequency, this study provides a preliminary foundation for further studies to better assess the underlying mechanisms how hydrology affect the biogeochemical cycling of DOM in large rivers.

Nonpher: computational method for design of hard-to-synthesize structures

In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can’t be easily assessed. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.

Alkynoates as Versatile and Powerful Chemical Tools for the Rapid Assembly of Diverse Heterocycles under Transition-Metal Catalysis: Recent Developments and Challenges

Heterocycles, heteroaromatics and spirocyclic entities are ubiquitous components of a wide plethora of synthetic drugs, biologically active natural products, marketed pharmaceuticals and agrochemical targets. Recognizing their high proportion in drugs and rich pharmacological potential, these invaluable structural motifs have garnered significant interest, thus enabling the development of efficient catalytic methodologies providing access to architecturally complex and diverse molecules with high atom-economy and low cost. These chemical processes not only allow the formation of diverse heterocycles but also utilize a range of flexible and easily accessible building units in a single operation to discover diversity-oriented synthetic approaches. Alkynoates are significantly important, diverse and powerful building blocks in organic chemistry due to their unique and inherent properties such as the electronic bias on carbon-carbon triple bonds posed by electron-withdrawing groups or the metallic coordination site provided by carbonyl groups. The present review highlights the comprehensive picture of the utility of alkynoates (2007-2019) for the synthesis of various heterocycles (> 50 types) using transition-metal catalysts (Ru, Rh, Pd, Ir, Ag, Au, Pt, Cu, Mn, Fe) in various forms. The valuable function of versatile alkynoates (bearing multifunctional groups) as simple and useful starting materials is explored, thus cyclizing with an array of coupling partners to deliver a broad range of oxygen-, nitrogen-, sulfur-containing heterocycles alongside fused-, and spiro-heterocyclic compounds. In addition, these examples will also focus the scope and reaction limitations, as well as mechanistic investigations into the synthesis of these heterocycles. The biological significance will also be discussed, citing relevant examples of drug molecules highlighting each class of heterocycles. This review summarizes the recent developments in the synthetic methods for the synthesis of various heterocycles using alkynoates as readily available starting materials under transition-metal catalysis.

Maximizing Step-Normalized Increases in Molecular Complexity: Formal [4+2+2+2] Photoinduced Cyclization Cascade to Access Polyheterocycles Possessing Privileged Substructures

A new complexity building photoinduced cascade which amounts to an unprecedented formal [4+2+2+2] cycloaddition topology is developed to access complex nitrogen polyheterocycles. This photocascade is initiated by the excited state intramolecular proton transfer (ESIPT) in aromatic amino ketones with tethered dual unsaturated pendants, i.e. pyrrole and alkenic moieties, resulting in the formation of four σ-bonds and setting six new stereogenic centers in a single experimentally simple photochemical step with up to 220 mcbit complexity increases.

Comparing the complexity of written and molecular symbolic systems

Symbolic systems (SSs) are uniquely products of living systems, such that symbolism and life may be inextricably intertwined phenomena. Within a given SS, there is a range of symbol complexity over which signaling is functionally optimized. This range exists relative to a complex and potentially infinitely large background of latent, unused symbol space. Understanding how symbol sets sample this latent space is relevant to diverse fields including biochemistry and linguistics. We quantitatively explored the graphic complexity of two biosemiotic systems: genetically encoded amino acids (GEAAs) and written language. Molecular and graphical notions of complexity are highly correlated for GEAAs and written language. Symbol sets are generally neither minimally nor maximally complex relative to their latent spaces, but exist across an objectively definable distribution, with the GEAAs having especially low complexity. The selection pressures guiding these disparate systems are explicable by symbol production and disambiguation efficiency. These selection pressures may be universal, offer a quantifiable metric for comparison, and suggest that all life in the Universe may discover optimal symbol set complexity distributions with respect to their latent spaces. If so, the "complexity" of individual components of SSs may not be as strong a biomarker as symbol set complexity distribution.

GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts

In this work, inspired by the graph transformer, we presented an improved protocol, termed GT-NMR, which integrates 2D molecular graph representation with Transformer architecture, for accurate yet efficient prediction of NMR chemical shifts. The effectiveness of the GT-NMR was thoroughly examined with the standard nmrshiftdb2 dataset, 37 natural products and structural elucidation of 11 pairs of natural products. Systematical analysis affirms that GT-NMR outperforms traditional graph-based methods in all aspects, achieving state-of-the-art performance, with the mean absolute error of 0.158 and 1.189 ppm in predicting 1H and 13C NMR chemical shifts, respectively, for the standard nmrshiftdb2 dataset. Further scrutiny of its practical applications indicates that GT-NMR's efficacy is closely tied to molecular complexity, as quantified by the size-normalized spatial score (nSPS). For relatively simple molecules (nSPS < = 27.71), GT-NMR performs comparably to the best density functional while its effectiveness significantly diminishes with complex molecules characterized by higher nSPS values (nSPS > = 38.42). This trend is consistent across other graph-based NMR chemical shift prediction methods as well. Therefore, while employing GT-NMR or other graph-based methods for the rapid and routine prediction of NMR chemical shifts, it is advisable to utilize nSPS to assess their suitability. The source codes and trained model of GT-NMR are publicly available at GitHub.Scientific contributionGT-NMR, which combines the 2D molecular graph representation with the Transformer architecture, was implemented for the first time to predict atom-level NMR chemical shifts, achieving state-of-the-art performance. More importantly, the reliability of the GT-NMR and graph-based methods was assessed for the first time in terms of molecular complexity, as quantified by the size-normalized spacial score (nSPS). Systematical scrutiny demonstrated that GT-NMR offer a valuable way for routine application in structural screening and elucidation of relatively simple molecules.

Recent Progress on NHC-Catalyzed 1,6-Conjugate Addition Reactions

As a significant variant of the Michael reaction, the 1,6-addition reaction has undergone considerable development over the past decade. This effective strategy enables the synthesis of a variety of novel and potentially bioactive functional molecules. In this review, we summarize the recent progress in NHC-catalyzed 1,6-addition reactions, highlighting their efficiency in the rapid synthesis of complex functional molecules. We also provide our perspectives on the future development of this dynamic and highly active research area.