Vibin Ipe Thomas

Sustainable valorization of glycerol to lactic acid ( LA ) through catalytic dehydrogenation addresses both waste minimization and value addition.

The Sonogashira type cross‐coupling reaction constitutes a pivotal method for constructing C(sp)–C(sp) bonds, enabling the synthesis of structurally diverse aryl alkynes with broad applications in pharmaceuticals and materials science. In the present study, a comprehensive mechanistic investigation of the iron‐catalysed Sonogashira‐type coupling between phenylacetylene and 4‐iodoacetophenone was performed employing density functional theory (DFT) calculations. The catalytic system, comprising FeCl6HO and 1,10‐phenanthroline in aqueous medium under aerobic conditions, was examined in detail. Our results elucidate a mechanistic pathway that proceeds via an initial formation of the pre‐catalytic species through an interaction between the metal iron and the ligand phenanthroline, followed by base‐assisted activation of the terminal alkyne to form the active catalytic species, culminating in C–C bond formation through a concerted nucleophilic substitution process. This study reveals a feasible redox‐neutral mechanistic pathway where the iron centre remains in its +3 oxidation state throughout the reaction cycle and thereby provides valuable insights into the reactivity of the iron‐catalysed Sonogashira coupling reaction.

Emerging pandemics are often driven by viral spillover events. However, predicting them remains difficult due to the complex interplay between viral genetics, host adaptability, and ecological dynamics. While traditional models rely on ecological or host traits, recent advances using viral sequence data and machine learning approaches have improved prediction accuracy. However, these approaches often lack a unified framework applicable across diverse viral families and host kingdoms, and are further constrained by the limited availability of comprehensive datasets. Here, we introduce SPHAK, a simple protein-based sequence similarity framework that could quantify spillover risk and predict viral family. By focusing on proteins, SPHAK is found to be a more effective way for identifying key amino acid patterns that can distinguish viral hosts. Since proteins are directly involved in infection, they could serve as a more informative probe than genome sequences. SPHAK is both accurate and generalizable, enabling its application across diverse viral families in animal and plant hosts. Application to influenza virus sequences validates the framework's effectiveness, with SPHAK successfully distinguishing spillover-associated protein signatures across multiple pandemic-relevant subtypes, including H1N1, H3N2, and H5N1. The predictive capacity of SPHAK supports its use in early warning systems and targeted surveillance, offering a practical tool to enhance pandemic preparedness and response.

Imidazo[1,2‐]pyridine derivatives have garnered significant attention due to their diverse pharmacological applications, driving the development of several synthetic methodologies. Among the various strategies employed, transition metal–catalyzed condensation reactions, particularly those of the Ortoleva–King type, have emerged as a prominent approach for the synthesis of imidazo[1,2‐]pyridines. Despite the ongoing experimental studies, a detailed molecular mechanistic investigation of these transformations remain insufficiently elucidated. In this study, we present a comprehensive computational investigation of an Fe/I assisted Ortoleva–King type reaction, employing aminopyridine and acetophenone as substrates. Using density functional theory (DFT) calculations at the unrestricted M06‐L/def2‐SVP level of theory, we delineate a five‐step mechanistic pathway: activation of the methyl group of acetophenone by aminopyridine in the presence of molecular iodine, nucleophilic addition of 2‐aminopyridine, redox reaction, water‐mediated cyclization to construct the imidazo core, and final dehydration to furnish the imidazo[1,2‐]pyridine scaffold. Particular emphasis is placed on elucidating the role of the iron cocatalyst, the surprising participation of water in promoting cyclization in a slightly polar medium such as chlorobenzene. We find that the presence of electron‐withdrawing groups on substrates has a substantial role in increasing the plausibility of the reaction.

Machine learning (ML) is revolutionizing various scientific fields, including chemistry. Among different ML techniques, graph neural networks (GNNs) have emerged as a powerful tool for capturing complex relationships in data by representing datapoints as graphs. Applying GNNs for predicting yields in chemical reactions is one of the emerging areas of focus. However, handling heterogeneous datasets remains a key challenge. In this study, we utilized diverse datasets encompassing various transition metal-catalyzed cross-coupling reactions including Suzuki, Sonogashira, Cadiot–Chodkiewicz, Ullmann-type, and Buchwald–Hartwig coupling reactions. To predict reaction yields, we implement multiple GNN architectures, including message passing neural networks (MPNN), residual graph convolutional networks (ResGCN), graph sample and aggregate (GraphSAGE), graph attention networks (GAT and GATv2), graph convolutional networks (GCN), and graph isomorphism networks (GIN). The comparative analysis of these architectures reveals that MPNN achieve the highest predictive performance, with an R² value of 0.75. Additionally, to enhance the interpretability of our models, we employed the integrated gradients method to determine the contribution of each input descriptor to the model's yield prediction. This study highlights the potential of effective and explainable graph-based models to predict the yields of chemical reactions. This work contributes to the application of machine learning in catalysis, providing valuable insights for optimizing catalytic reactions and contributing to innovations in sustainable chemistry and organic synthesis.

The Suzuki–Miyaura Coupling (SMC) reaction is a powerful method for forming carbon–carbon bonds in organic synthesis. Recent advancements in SMC reactions have introduced first-row transition metal catalysts, with zinc garnering significant interest due to its cost-effective and eco-friendly nature. Despite progress in experimental protocols, the mechanistic details of zinc-catalyzed SMC reactions are limited. This study explores the mechanism of Zn-catalyzed SMC reactions between alkynyl halides and aryl boronic acids using density functional theory. A four-coordinated N,N′-dimethylethylenediamine (DMEDA) ligated Zn(II) complex is identified as the active catalyst. Unlike Pd-catalyzed SMC, the mechanism proceeds via an initial transmetalation process forming aryl zincates. Further, the activation of organic halide occurs through a redox-neutral pathway involving a concerted nucleophilic substitution-reductive elimination process, eliminating the cross-coupled product while regenerating the active catalyst. The energy span (27.2 kcal mol⁻¹) for the process concords with the temperature requirements (80 °C) in the experiment. The activation of organic halide is identified as the turnover-limiting step. The unconventional redox-neutral mechanism could be rationalized by the stable d¹⁰ configuration at the Zn(II) center and the ease of bond formation between the coupling partners. This computational study thereby provides new mechanistic insights into Suzuki cross-coupling reactions, aiding the synthesis of novel functional scaffolds using eco-friendly methods.

DFT studies suggest a redox-neutral mechanism proceeding via σ-bond metathesis as the most plausible catalytic pathway for Zn( ii )- and Mn( ii )-catalyzed O -arylation reactions.