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Comments on “Does φ-Aromaticity exist in prismatic {Bi6}-based clusters?”
No full textWe read the manuscript by Dariusz W. Szczepanik and Miquel Solà with interest, and recognized several misinterpretations (based on oversimplifications) of our work and also errors that results from inappropriate/insufficient methods applied in their follow-up studies. This led to erroneous statements, which the authors additionally mixed with statements on aromaticity, which does not fully comply with definitions that have been well-established, e.g., for benzene. In this comment, we outline the misinterpretations, errors, and questionable statements, thereby referring to our work and further literature to underline the facts
High-throughput Quantum Theory of Atoms in Molecules (QTAIM) for Geometric Deep Learning of Molecular and Reaction Properties
No full textWe present a package, Generator, for geometric molecular property prediction based on topological features of quantum mechanical electron density. Generator computes Quantum Theory of Atoms in Molecules (QTAIM) features, at density functional theory (DFT) level, for sets of molecules or reac- tions in a high-throughput manner, and compiles features into a single data structure for processing, analysis, and geometric machine learning. An accompanying graph neural network package can be used for property prediction and allows users to readily use computed features for learning tasks. To test the efficacy of electron density-based data for machine learning, we benchmark several datasets including QM8, QM9, LIBE, Tox21, and a Green 2022 Reaction dataset. This wide dataset diversity underscores the flexibility of QTAIM descriptors and our package. In addition, we made our code high-throughput methods compatible with new versions of BondNet and Chemprop architectures to allow for both reaction and molecular property prediction out-of-the-box. To motivate the use of QTAIM features for varied prediction tasks we also perform extensive benchmarking of our new mod- els to existing benchmark models as well as to our own models without QTAIM features. We show that almost universally, QTAIM features improve model performance on our algorithms, ChemProp, and BondNet. We also determine that QTAIM can aid in generalizing model performance to out-of- domain (OOD) datasets and improve learning at smaller data regimes. Combined, we hope that this framework could enable QTAIM-enhanced structure-to-property predictions - especially in domains with less data, including experimental or reaction-level datasets with complex underlying chemistrie
AI-driven drug discovery: identification and optimization of ALDH3A1 selective inhibitors with nanomolar activity
No full textIn the field of medicinal chemistry, the discovery of novel compounds with therapeutic potential is of utmost importance. However, the conventional approach to drug discovery, relying on high-throughput screening (HTS), encounters limitations such as reagent availability and labor costs. Although a hit-finding campaign starts with a virtual screen of millions of compounds, the hit-to-lead and lead optimization stages often require designing, synthesizing, and profiling thousands of analogs before selecting clinical candidates. Recent advances offer an innovative solution - the virtual generation of billions of synthetically feasible compounds with an impressive 80% success rate for synthesis. This breakthrough has the potential to significantly expand the chemical space available for purchase and experimental validation, bringing about a revolution in the field. Our study presents a comprehensive approach to compound discovery and optimization, harnessing quantitative high-throughput screening (qHTS), chemical databases, and reaction-based enumeration. To further enhance the synthesis process and gain deeper insights into compound formation, we utilize the Reaction Cookbook from Biosolveit, which comprises reaction SMARTs for around 300 chemical reactions. By employing this wide range of reactions, we aim to uncover the full spectrum of a chemotype\u27s potential and customize its structure to optimize desired properties. As an illustration of this approach, our work on the ALDH3A1 project resulted in the synthesis of 50 compounds, with 21 of them exhibiting activity (negative curve class values; hit-rate ~42%). Among these active compounds, 6 displayed IC50 values lower than 30 µM and efficacy values less than -50%, with the most potent compound achieving an impressive potency of 447 nM. This study demonstrates the successful synergy between in-silico reaction-based analogs enumeration, molecular modeling and AI/ML-based techniques in identifying compounds with improved biological activity, offering promising prospects for the development of ALDH3A1-targeting agents as potential cancer therapeutics. Computational workflows developed in this study can be used for similar target-based drug discovery campaigns
Unlocking the Secrets of Porous Silicon Formation: Insights into Magnesiothermic Reduction Mechanism using In-situ Powder X-ray Diffraction Studies
No full textThe magnesiothermic reduction of SiO2 is an important reaction as it is a bulk method that produces porous Si for a wide range of applications directly from SiO2. While its main advantage is potential tunability, the reaction behavior and final product properties are heavily dependent on many parameters including feedstock type. However, a complete understanding of the reaction pathway has not yet been achieved. Here, using in-situ X-ray diffraction analysis, we map for the first time, various pathways through which the magnesiothermic reduction reaction proceeds. Further, we identified the key parameters and conditions that determine which pathways are favored. We discovered that the reaction onset temperatures can be as low as 348 ± 7°C, which is significantly lower than previously reported values. The onset temperature is dependent on the size of Mg particles. Further, Mg2Si was identified as a key intermediate rather than a reaction byproduct during the reduction process. Its rate of consumption is determined by the reaction temperature which needs to be >535°C. These findings can enable process and product optimization of the magnesiothermic reduction process to manufacture and tune porous Si for a range of applications
Oxide Encapsulated Ruthenium Oxide Catalysts for Selective Oxygen Evolution in Unbuffered pH Neutral Seawater
No full textDirect seawater electrolysis is a promising approach to producing green hydrogen in water-scarce environments using renewable energy. However, the undesirable chlorine evolution reaction (CER) and hypochlorite evolution reaction (HCER) compete with the desired oxygen evolution reaction (OER) at the anode electrocatalyst. This issue is most pronounced in unbuffered pH neutral solutions due to local acidification resulting from the OER. To overcome this challenge, this study explores the use of silicon oxide (SiOx) and titanium oxide (TiOx) nanoscale overlayers coated on metallic ruthenium (Ru) and ruthenium oxide (RuOx) thin film electrodes to block chloride ions from reaching active sites during operation in unbuffered 0.6 M NaCl electrolyte. Using a combination of (electro)analytical techniques, encapsulated RuOx anodes are shown to effectively suppress Cl- transport to buried catalyst active sites while allowing for the desired OER to occur, leading to increases in OER faradaic efficiency at moderate overpotentials. Evidence for the ability of SiOx overlayers to block Cl- ions from reaching the active buried interface was obtained by monitoring the OH stretching mode of OH adsorbates using in situ Raman spectroscopy. This study also reports trade-offs between the activity, selectivity, and stability of bare and encapsulated Ru and RuOx electrocatalysts, finding that the magnitude of these trade-offs strongly depends on the nature of both the catalyst and overlayer material. The most promising anode electrocatalyst is RuOx encapsulated by 4 nm of SiOx, which gives the largest improvement in OER faradaic efficiency while demonstrating a relatively stable operating current and minimal increases in overpotential
Proton transport in water is doubly gated by sequential hydrogen-bond exchanges
No full textThe transport of excess protons in water is central to acid-base chemistry, biochemistry, and energy production. However, elucidating its mechanism has been challenging. Recent nonlinear vibrational spectroscopy experiments could not be explained by existing models. Here, we combine neural network-based molecular dynamics simulations considering nuclear quantum effects for all atoms and vibrational spectroscopy calculations to determine the proton transport mechanism. Our simulations reveal the equilibrium between two stable proton-localized structures with distinct Eigen-like and Zundel-like hydrogen-bond motifs. Proton transport follows a three-step mechanism gated by two successive hydrogen-bond exchanges: the first reduces the proton-acceptor water coordination, leading to proton transfer, and the second, the rate-limiting step, prevents rapid back-transfer by increasing the proton-donor coordination. This sequential mechanism is consistent with experimental characterizations of proton diffusion, explaining the low activation energy and the prolonged intermediate lifetimes in vibrational spectroscopy. These results are crucial for understanding proton dynamics in biochemical and technological systems
Nylon Analogue Substrates Allow for Continuous Quantification of Polyamidase Activity in Nylon-Degrading Enzymes
No full textNylon-hydrolyzing enzymes have been of increased interest recently in the context of bioremediation. The aminohexanoate oligomer hydrolases (NylCs) are thus far the most promising biocatalysts identified to this end. Protein engineering has been used to increase the thermal stability of these enzymes, but relatively little work has been done to improve their catalytic activity, due in part to a lack of high-throughput assays. Herein we report the design, synthesis, and enzymatic hydrolysis of polyamide analogue substrates mimicking various nylon architectures. We observed hydrolysis of diamide analogues 2, 4, and 5 in a continuous and quantitative manner via a light-scattering assay, which is amenable to a high-throughput screen in 96-well plates. The reaction products were characterized by liquid chromatography-coupled mass spectrometry, revealing insight into the structural elements required for recognition of substrates by NylC enzymes. The assay may be performed in minutes at elevated temperatures, allowing for efficient screening of thermostable nylonase enzymes. The activity of the NylC enzymes towards substrate 2 correlated to their corresponding enzymatic hydrolysis of Nylon-6 film, indicating that these substrates are surrogates for bulk nylon hydrolysis. Finally, we demonstrate the applicability of this assay to cell lysate, further enabling protein engineering efforts
A Review for Nanocellulose-based Materials in Water Treatment
No full textNanocellulose, which refers to cellulose at the nanoscale, has been extensively employed for water treatment. It can be synthesized in diverse formats, such as colloidal powders that can be easily dispersed in water, films, membranes, nanosheets, hydrogels/aerogels, and three-dimensional (3D) scaffolds. Their claimed activity is the elimination of water pollutants such as heavy metals, dyes, pesticides, pharmaceuticals, microbiological cells, and other pollutants from water systems. This article provides a concise overview of the latest advancements in water treatment utilizing nanocellulose-based materials. The study also incorporated a scientometric analysis of the topic. Cellulose-based compounds facilitate the elimination of water pollutants, while salts provide sophisticated techniques for water desalination. They are extensively utilized as substrates, adsorbents, and catalysts. These methods were used to remove pollutants, including adsorption, filtration, disinfection, coagulation/flocculation, chemical precipitation, sedimentation, Reverse Osmosis (RO), ultrafiltration (UF), nanofiltration (NF), electrofiltration (Electrodialysis), ion-exchange, chelation, catalysis, and photocatalysis. The conversion of cellulose into commercial goods facilitates the extensive utilization of nanocellulose-based materials as adsorbents and catalysts
Revealing Ion Adsorption and Charging Mechanisms in Layered Metal-Organic Framework Supercapacitors with Solid-State Nuclear Magnetic Resonance
No full textConductive layered metal-organic frameworks (MOFs) have demonstrated promising electrochemical performances as supercapacitor electrode materials. The well-defined chemical structures of these crystalline porous electrodes facilitate structure-performance studies, however there is a fundamental lack in the molecular-level understanding of charge storage mechanisms in conductive layered MOFs. To address this, we employ solid-state nuclear magnetic resonance (NMR) spectroscopy to study ion adsorption in nickel 2,3,6,7,10,11-hexaiminotriphenylene, Ni3(HITP)2. In this system, we find that separate resonances can be observed for the MOF’s in-pore and ex-pore ions. The chemical shift of in-pore electrolyte is found to be dominated by specific chemical interactions with the MOF functional groups, with this result supported by quantum-mechanics/molecular-mechanics (QM/MM) and density functional theory (DFT) calculations. Quantification of the electrolyte environments by NMR was also found to provide a proxy for electrochemical performance, which could facilitate the rapid screening of synthesised MOF samples. Finally, the charge storage mechanism was explored using a combination of ex-situ NMR and operando electrochemical quartz-crystal microbalance (EQCM) experiments. These measurements revealed that cations are the dominant contributor to charge storage in Ni3(HITP)2, with anions contributing only a minor contribution to the charge storage. Overall, this work establishes the methods for studying MOF-electrolyte interactions via NMR spectroscopy. Understanding how these interactions influence the charging storage mechanism will aid the design of MOF-electrolyte combinations to optimise the performance of supercapacitors, as well as other electrochemical devices including electrocatalysts and sensors
Design Green Chemicals by Predicting Vaporization Properties Using Explainable Graph Attention Networks
No full textComputational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable. Here, we developed chemically explainable graph attention networks to predict five physical properties pertinent to performance in utilizing renewable energy: heat of vaporization (HoV), critical temperature, flash point, boiling point, and liquid heat capacity. The predictive model for HoV was trained using ~150,000 data points, considering their uncertainties and temperature dependence. Next, this model was expanded to the other properties through transfer learning to overcome the limitations due to fewer data points (700-7,500). The chemical interpretability of the model was then investigated, demonstrating that the model explains molecular structural effects on vaporization properties. Finally, the developed predictive models were applied to design chemicals that have desirable properties as efficient and green working fluids, fuels, and polymers, enabling fast and accurate screening before experiments