Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .

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Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a \"bending-down\" binding pose at 5-HT2AR to act as an antagonist and a \"stretching-up\" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the \"agonist filter\" and \"conformation shaper,\" which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.

The therapeutic effects of Chinese herbal compounds are often achieved through the synergistic interactions of multiple ingredients. However, current research predominantly focuses on individual ingredients, neglecting the holistic nature of Chinese herbal compounds. This study proposes a novel strategy to elucidate the pharmacodynamic material basis of Chinese herbal compounds based on their multi-components (components named \'ZuFen\' in China, it refers to multiple ingredients with similar chemical structures) composition, using the Xian-Ling-Gu-Bao (XLGB) capsule as a case study. Cheminformatics-based components partitioning was conducted after sourcing ingredients from various databases, resulting in a total of 856 ingredients which were categorized into nine major components. Furthermore, the pharmacodynamic ingredients of XLGB capsule were determined by analyzing the ingredients that were absorbed into the bloodstream. Through a combination of these ingredients and screening for absorption, the Dipsacus asper saponin components, Psoralea corylifolia coumarin components, and Epimedium flavonoid polyglycosides components were isolated. The anti-osteoporosis efficacy of these components were evaluated in zebrafish, demonstrating their capability to reverse mineralization reduction caused by prednisolone. These findings further support the idea that these components serve as the material basis for the pharmacological efficacy of XLGB capsule. This study provides a novel systematic strategy for discovering the pharmacodynamic material basis of the efficacy of Chinese herbal compounds based on a \'multi-components\' perspective.

Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.

Rare actinomycetes are highly valued as potential sources of novel bioactive secondary metabolites. Among these rare actinomycetes, the genus Saccharothrix is particularly noteworthy due to its ability to produce a diverse range of bioactive secondary metabolites. With the continuous sequencing of bacterial genomes and the rapid development of bioinformatics technologies, our knowledge of the secondary metabolic potential of Saccharothrix can become more comprehensive, but this space has not been reviewed or explored. This review presents a detailed overview of the chemical structures and bioactivities of 138 Saccharothrix-derived secondary metabolites, which are classified into five distinct groups based on their biosynthetic pathways. Furthermore, we delve into experimentally characterized biosynthetic pathways of nine bioactive metabolites. By utilizing a combination of cheminformatic and bioinformatic approaches, we attempted to establish connections between the metabolite families and the biosynthetic gene cluster families encoded by Saccharothrix strains. Our analysis provides a comprehensive perspective on the secondary metabolites that can be linked to corresponding BGCs and highlights the underexplored biosynthetic potential of Saccharothrix. This review also provides guidance for the targeted discovery and biosynthesis of novel natural products from Saccharothrix.

Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.

Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Recent studies suggest RNAs act as promising drug targets. However, limited development has been achieved in detecting RNA-ligand interactions. To guide the discovery of RNA-binding ligands, it is necessary to characterize them comprehensively, especially in the binding specificity, binding affinity and drug-like properties. We established a database, RNALID (http://biomed.nscc-gz.cn/RNALID/html/index.html#/database), which collects RNA-ligand interactions validated by low-throughput experiment. RNALID contains 358 RNA-ligand interactions. Comparing to the fellow database, 94.5% of ligands in RNALID are completely or partially novel collections, and 51.78% have novel two-dimensional (2D) structures. Through the analysis of ligand structure, binding affinity and cheminformatic parameters we found that multivalent (MV) ligands mainly binding to RNA repeats are more structurally conserved in both 2D and 3D structures than other ligand types, exhibit higher binding specificity and binding affinity than ligands binding to non-repeat RNAs, but deviate far from the Lipinski\'s rule of five. In contrary, small molecule (SM) ligands binding to virus RNA exhibit higher affinity and more resemble protein-ligands, but potentially possess low binding specificity. Further analysis on 28 detailed drug-likeness properties indicated that RNA-ligands\' development need to balance between the binding affinity and the drug-likeness because of the significant linear co-relationship between the two. Comparing RNALID ligands to FDA-approved drugs and ligands without bioactivity indicated that RNA-binding ligands are different from them in chemical properties, structural properties and drug-likeness. Thus, characterizing the RNA-ligand interactions in RNALID in multiple respects provides new insights into discovering and designing druggable ligands binding with RNA.