The typical way in which lead optimisation (LO) series are represented in the medicinal chemistry literature is as Markush structures and associated R-group tables. The Markush structure shows a central core or molecular scaffold that is common to the series with R groups that indicate the points of variability that have been explored in the series. The associated R-group table shows the substituent combinations that exist in individual molecules in the series together with properties of those compounds. This format provides an intuitive way of visualising any structure–activity relationship (SAR) that is present. Automated approaches that attempt to reproduce this well understood format, such as the SAR map, are based on maximum common substructure approaches and do not take account of small changes that may be made to the core structure itself or of the situation where more than one core exists in the data. Here we describe an automated approach to represent LO series that is based on reduced graph descriptions of molecules. A publicly available LO dataset from a drug discovery programme at GSK is analysed to show how the method can group together compounds from the same series even when there are small substructural differences within the core of the series while also being able to identify different related compound series. The resulting visualisation is useful in identifying areas where series are under explored and for mapping design ideas onto the current dataset. The code to generate the visualisations is released into the public domain to promote further research in this area.
Scientific contribution: We describe a software tool for analysing lead optimisation series using reduced graph representations of molecules. The representation allows compounds that have similar but not identical chemical scaffolds to be grouped together and is, therefore, an advance on methods that are based on the more traditional Markush structure and SAR tables. The software is a useful addition to the med chem toolbox as it can provide a holistic view of lead optimisation data by representing what might otherwise be seen as separate series as a single series of compounds.
The development of heterostructures with stabilized heterogeneous structures is crucial for the improvement of photocatalytic activity and practical applications. In this study, a S-type heterojunction of γ-Bi2O3/BiOBr was synthesized by a simple hydrothermal method. Under simulated sunlight, the degradation ratio of phenol can attain nearly 91.75% for 17%-γ-Bi2O3/BiOBr heterojunction after 120 min, while only 2.8% and 52.86% for pure γ-Bi2O3 and BiOBr, respectively. Meanwhile, its first-order reaction rate is 3.46 and 22.81 times more than those of pure BiOBr and γ-Bi2O3, respectively. In addition, the 17%-γ-Bi2O3/BiOBr heterojunction exhibits the excellent cycle stability, as its phenol degradation ratio can retain nearly 86% after five cycle experiments. The heterojunction was analyzed as an S-type heterojunction based on XPS, EPR and free radical trapping experiments. The performance enhancement of the catalyst is thus due to the formation of an S-type heterojunction, which reduces the recombination rate of photogenerated electrons and photogenerated holes and promotes the formation of active species, thus dramatically increasing the efficiency of photocatalytic degradation of phenol.
In this study, the core and periphery of the B13+ cluster have been unveiled by employing a novel approach—Reduced Electron Density Analysis of adaptive natural density partitioning (AdNDP) Orbitals—to explore its rotor action. The central core of the cluster, acting as a “control unit”, governs the transformation between the ground state (GS) and transition state (TS). Core–peripheral electron density separation alongside AdNDP analysis has revealed how electron density shifts within the core dictate the cluster's structural transitions. For the first time, the reduced electron density of AdNDP orbitals provides a clearer visualization of the core's subtle rotational movements, offering an unprecedented look at the mechanism behind the GS-TS interconversion. The iso-surface plots highlight the influence of three core atoms, particularly one in the GS and two atoms in the TS, which serve as “master atoms” guiding the transformation. This work introduces a new methodology for investigating nanoscale transformations, laying the groundwork for future research in controlling nanomotors and designing advanced materials.
The rise in drug-resistant fungal infections has intensified the need for novel antifungal agents, particularly those targeting cytochrome P450 51 (CYP51) a key enzyme in ergosterol biosynthesis. This study explores the design, synthesis, and biological evaluation of 1H-indazole derivatives as potential CYP51 inhibitors. Structure-based virtual screening identified seventeen indazole-based candidates, which were further assessed for pharmacokinetic properties including absorption, distribution, metabolism, excretion, and toxicity (ADMET) using computational approaches. Two lead compounds, 1aa and 2aa were synthesized and evaluated for their antifungal activity against Rhizopus oryzae. Among them, compound 1aa exhibited superior antifungal efficacy with a minimum inhibitory concentration (MIC) of 128 µg/mL, while 2aa displayed an MIC of 256 µg/mL. In vitro assays confirmed that 1aa effectively inhibited ergosterol biosynthesis correlating with its strong binding affinity to the heme group of CYP51, as demonstrated by molecular docking and molecular dynamics simulations. These findings highlight 1aa as a promising lead compound for further optimization offering a potential strategy to combat antifungal resistance in clinical settings.