Matlab图形用户界面辅助《化学信息学》教学

化学信息学是一门利用信息学、计算机科学和技术来解决化学问题的学科。本文利用Matlab科学计算软件的图形用户界面(GUI)功能,通过分子模拟课堂教学的具体实例来展示图形用户界面在化学信息学课程辅助教学中的优点。交互式图形用户界面使得分子模拟过程直观可视化,通过理论与实践教学的结合有助于提高教学质量,激发学习兴趣。     

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K_i ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.     

New inhibitors of the Tat-TAR RNA interaction found with a "fuzzy" pharmacophore model

TAR RNA is a potential target for AIDS therapy. Ligand-based virtual screening was performed to retrieve novel scaffolds for RNA-binding molecules capable of inhibiting the Tat-TAR interaction, which is essential for HIV replication. We used a "fuzzy" pharmacophore approach (SQUID) and an alignment-free pharmacophore method (CATS3D) to carry out virtual screening of a vendor database of small molecules and to perform "scaffold-hopping". A small subset of 19 candidate molecules were experimentally tested for TAR RNA binding in a fluorescence resonance energy transfer (FRET) assay. Both methods retrieved molecules that exhibited activities comparable to those of the reference molecules acetylpromazine and chlorpromazine, with the best molecule showing ten times better binding behavior (IC50 = 46 microM). The hits had molecular scaffolds different from those of the reference molecules.     

Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms

By the use of knowledge gained through modeling of drug metabolism mediated by the cytochrome P450 2D6 and 3A4 isoforms, we constructed a 2D-based model for site-of-metabolism prediction for the cytochrome P450 2C9 isoform. The similarities and differences between the models for the 2C9 and 2D6 isoforms are discussed through structural knowledge from the X-ray crystal structures and trends in experimental data. The final model was validated on an independent test set, resulting in an area under the curve value of 0.92, and a site of metabolism was found among the top two ranked atoms for 77% of the compounds.     

A Consistent Dataset of Kinetic Solubilities for Early‐Phase Drug Discovery

Herein, we describe a new dataset of kinetic aqueous solubilities determined by nephelometry for 711 druglike compounds. The solubilities are reported in twelve classes ranging from <2 μg mL^?1 to >250 μg mL^?1. The measurements were designed to provide the appropriate data for applications in the early phases of drug discovery. Three class classification models (insoluble, moderately soluble, soluble) were built using the random forest algorithm and their performance for this dataset was analyzed.     

Automated Identification of Depsipeptide Natural Products by an Informatic Search Algorithm

Nonribosomal depsipeptides are a class of potent microbial natural products, which include several clinically approved pharmaceutical agents. Genome sequencing has revealed a large number of uninvestigated natural‐product biosynthetic gene clusters. However, while novel informatic search methods to access these gene clusters have been developed to identify peptide natural products, depsipeptide detection has proven challenging. Herein, we present an improved version of our informatic search algorithm for natural products (iSNAP), which facilitates the detection of known and genetically predicted depsipeptides in complex microbial culture extracts. We validated this technology by identifying several depsipeptides from novel producers, and located a large number of novel depsipeptide gene clusters for future study. This approach highlights the value of chemoinformatic search methods for the discovery of genetically encoded metabolites by targeting specific areas of chemical space.