VNP: Interactive Visual Network Pharmacology of Diseases, Targets, and Drugs.

In drug discovery, promiscuous targets, multifactorial diseases, and "dirty" drugs construct complex network relationships. Network pharmacology description and analysis not only give a systems-level understanding of drug action and disease complexity but can also help to improve the efficiency of target selection and drug design. Visual network pharmacology (VNP) is developed to visualize network pharmacology of targets, diseases, and drugs with a graph network by using disease, target or drug names, chemical structures, or protein sequence. To our knowledge, VNP is the first free interactive VNP server that should be very helpful for systems pharmacology research. VNP is freely available at http://cadd.whu.edu.cn/ditad/vnpsearch.

Transferability of retrotransposon primers derived from Persimmon (Diospyros kaki Thunb.) across other plant species.

Retrotransposon-based molecular markers are powerful molecular tools. However, these markers are not readily available due to the difficulty in obtaining species-specific retrotransposon primers. Although recent techniques enabling the rapid isolation of retrotransposon sequences have facilitated primer development, this process nonetheless remains time-consuming and costly. Therefore, research into the transferability of retrotransposon primers developed from one plant species onto others would be of great value. The present study investigated the transferability of retrotransposon primers derived from 'Luotian-tianshi' persimmon (Diospyros kaki Thunb.) across other fruit crops, as well as within the genus using inter-retrotransposon amplified polymorphism molecular marker. Fourteen of the 26 retrotransposon primers tested (53.85%) produced robust and reproducible amplification products across all fruit crops tested, indicating their applicability across plant species. Four of the 13 fruit crops showed the best transferability performances: persimmon, grape, citrus, and peach. Furthermore, similarity coefficients and UPGMA clustering indicated that these primers could further offer a potential tool for germplasm differentiation, parentage identification, genetic diversity assessment, classification, and phylogenetic studies across a variety of plant species. Transferability was further confirmed by examining published primers derived from Rosaceae, Gramineae, and Solanaceae. This study is one of the few currently available studies concerning the transferability of retrotransposon primers across plant species in general, and is the first successful study of the transferability of retrotransposon primers derived from persimmon. The primers presented here will help reduce costs for future retrotransposon primer development and therefore contribute to the popularization of retrotransposon molecular markers.

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.