Air filtration has become an essential need for passive pollution control. However, most of the commercial air purifiers rely on dense fibrous filters, which have good particulate matter (PM) removal capability but poor biocidal effect. Here we present the photocatalytic bactericidal properties of a series of metal-organic frameworks (MOFs) and their potentials in air pollution control and personal protection. Specifically, a zinc-imidazolate MOF (ZIF-8) exhibits almost complete inactivation of Escherichia coli ( E. coli ) (>99.9999% inactivation efficiency) in saline within 2 h of simulated solar irradiation. Mechanistic studies indicate that photoelectrons trapped at Zn + centers within ZIF-8 via ligand to metal charge transfer (LMCT) are responsible for oxygen-reduction related reactive oxygen species (ROS) production, which is the dominant disinfection mechanism. Air filters fabricated from ZIF-8 show remarkable performance for integrated pollution control, with >99.99% photocatalytic killing efficiency against airborne bacteria in 30 min and 97% PM removal. This work may shed light on designing new porous solids with photocatalytic antibiotic capability for public health protection.

Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.

No abstract

Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACHIn this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTSHigh performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC 50 or EC 50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONSIn summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.Abbreviations bSDTNBI, balanced substructure-drug-target network-based inference; DTI, drug-target interaction; e P , precision enhancement; e R , recall enhancement; ERα, oestrogen receptor α; E2, estradiol; MoA, mechanism of action; NBI, network-based inference; P, precision; R, recall; ROC, receiver operating characteristic

No abstract

Rational self-assembly of hexaniobate Lindqvist-type precursor [HNb6O19]7- with soluble Cu2+ salts utilizing different strategies produces a series of giant polyniobate clusters, namely, (H2en)1.25[Cu(en)2(H2O)]2Cl4[Nb24O72H21.5]7 H2O (1; en: ethylenediamine), [Cu(en)2]3[Cu(en)2(H2O)]9[{H2Nb6O19} subset{[({KNb24O72H10.25}{Cu(en)2})2{Cu3(en)3(H2O)3}{Na1.5Cu1.5(H2O)8}{Cu(en)2}4]6}]144 H2O (2), K12Na4[H23NaO8Cu24(Nb7O22)8]106 H2O (3), and K16Na12[H9Cu25.5O8(Nb7O22)8] 73.5 H2O (4). Their structures were determined and further characterized by single-crystal X-ray diffraction analysis, IR and Raman spectroscopy, thermogravimetric analysis (TGA), and elemental analysis. Structural analyses reveal that compound 2 comprises a giant capsule anion based on a wheel-shaped cluster encapsulating a Lindqvist diprotonated cluster [H2Nb6O19]6- unit, and forms a honeycomb-like structure with the inclusion of Lindqvist-type anions [H2Nb6O19]6- in the holes, whereas 3 and 4 represent an unprecedented giant cube-shaped framework. All the compounds are built from [Nb7O22]9- fundamental building blocks. Solution Raman spectroscopy studies of 2 and 3 reveal that the solid-state structures of these polyniobate cluster anions disassemble and exist in the form of the [Nb6O19]8- unit in solution. Magnetic susceptibility measurement of 3 shows antiferromagnetic coupling interactions between CuII ions with the spin-canting phenomenon.

Elucidation of chemical-protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure-activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81 689 CPI pairs among 50 924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43 965 CPI pairs among 23 376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, , which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.

Recently, supervised machine learning has been ascending in providing new predictive approaches for chemical, biological, and materials sciences applications. In this Perspective, we focus on the interplay of machine learning methods with the chemically motivated descriptors and the size and type of datasets needed for molecular property prediction. Using nuclear magnetic resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data are abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning methods drawn from deep learning, random forests, or kernel methods.