Using small, flat aromatic rings as components of fragments or molecules is a common practice in fragment‐based drug discovery and lead optimization. With an increasing focus on the exploration of novel biological and chemical space, and their improved synthetic accessibility, 3D fragments are attracting increasing interest. This study presents a detailed analysis of 3D and 2D ring fragments in marketed drugs. Several measures of properties were used, such as the type of ring assemblies and molecular shapes. The study also took into account the relationship between protein classes targeted by each ring fragment, providing target‐specific information. The analysis shows the high structural and shape diversity of 3D ring systems and their importance in bioactive compounds. Major differences in 2D and 3D fragments are apparent in ligands that bind to the major drug targets such as GPCRs, ion channels, and enzymes.

Summary: ScaffoldGraph (SG) is an open-source Python library and command-line tool for the generation and analysis of molecular scaffold networks and trees, with the capability of processing large sets of input molecules. With the increase in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysis of chemical space, being used for visualization, clustering, scaffold-diversity analysis and active-series identification. Built on RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility and extendibility of the tool compared to existing software. Availability and implementation: SG is freely available and released under the MIT licence at https://github.com/UCLCheminformatics/ScaffoldGraph.

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

Inflammation and fibrosis are important components of diseases that contribute to the malfunction of epithelia and endothelia. The Rho guanine nucleotide exchange factor (GEF) GEF-H1/ARHGEF-2 is induced in disease and stimulates inflammatory and fibrotic processes, cell migration, and metastasis. Here, we have generated peptide inhibitors to block the function of GEF-H1. Inhibitors were designed using a structural in silico approach or by isolating an inhibitory sequence from the autoregulatory C-terminal domain. Candidate inhibitors were tested for their ability to block RhoA/GEF-H1 binding in vitro, and their potency and specificity in cell-based assays. Successful inhibitors were then evaluated in models of TGFβ-induced fibrosis, LPS-stimulated endothelial cell-cell junction disruption, and cell migration. Finally, the most potent inhibitor was successfully tested in an experimental retinal disease mouse model, in which it inhibited blood vessel leakage and ameliorated retinal inflammation when treatment was initiated after disease diagnosis. Thus, an antagonist that blocks GEF-H1 signaling effectively inhibits disease features in in vitro and in vivo disease models, demonstrating that GEF-H1 is an effective therapeutic target and establishing a new therapeutic approach.