Traditional Chinese medicine (TCM) is not only an effective solution for primary health care, but also a great resource for drug innovation and discovery. To meet the increasing needs for TCM-related data resources, we developed ETCM, an Encyclopedia of Traditional Chinese Medicine. ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients. The herb basic property and quality control standard, formula composition, ingredient drug-likeness, as well as many other information provided by ETCM can serve as a convenient resource for users to obtain thorough information about a herb or a formula. To facilitate functional and mechanistic studies of TCM, ETCM provides predicted target genes of TCM ingredients, herbs, and formulas, according to the chemical fingerprint similarity between TCM ingredients and known drugs. A systematic analysis function is also developed in ETCM, which allows users to explore the relationships or build networks among TCM herbs, formulas,ingredients, gene targets, and related pathways or diseases. ETCM is freely accessible at http://www.nrc.ac.cn:9090/ETCM/. We expect ETCM to develop into a major data warehouse for TCM and to promote TCM related researches and drug development in the future.

Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3β. Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0287-6) contains supplementary material, which is available to authorized users.

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 − 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of Θ( n/k) on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to com- * Author affiliations and emails: W. pute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.

Accumulating evidence suggested that an orphan G protein-coupled receptor (GPR)30, mediates nongenomic responses to estrogen. The present study was performed to investigate the molecular mechanisms underlying GPR30 function. We found that knockdown of GPR30 expression in breast cancer SK-BR-3 cells down-regulated the expression levels of estrogen receptor (ER)-alpha36, a variant of ER-alpha. Introduction of a GPR30 expression vector into GPR30 nonexpressing cells induced endogenous ER-alpha36 expression, and cotransfection assay demonstrated that GPR30 activated the promoter activity of ER-alpha36 via an activator protein 1 binding site. Both 17beta-estradiol (E2) and G1, a compound reported to be a selective GPR30 agonist, increased the phosphorylation levels of the MAPK/ERK1/2 in SK-BR-3 cells, which could be blocked by an anti-ER-alpha36-specific antibody against its ligand-binding domain. G1 induced activities mediated by ER-alpha36, such as transcription activation activity of a VP16-ER-alpha36 fusion protein and activation of the MAPK/ERK1/2 in ER-alpha36-expressing cells. ER-alpha36-expressing cells, but not the nonexpressing cells, displayed high-affinity, specific E2 and G1 binding, and E2- and G1-induced intracellular Ca(2+) mobilization only in ER-alpha36 expressing cells. Taken together, our results demonstrated that previously reported activities of GPR30 in response to estrogen were through its ability to induce ER-alpha36 expression. The selective G protein-coupled receptor (GPR)30 agonist G1 actually interacts with ER-alpha36. Thus, the ER-alpha variant ER-alpha36, not GPR30, is involved in nongenomic estrogen signaling.

Summary Cytochromes P450 (CYPs) play key role in generating the structural diversity of terpenoids, the largest group of plant natural products. However, functional characterization of CYPs has been challenging because of the expansive families found in plant genomes, diverse reactivity and inaccessibility of their substrates and products.Here we present the characterization of two CYPs, CYP76AH3 and CYP76AK1, that act sequentially to form a bifurcating pathway for the biosynthesis of tanshinones, the oxygenated diterpenoids from the Chinese medicinal plant Danshen.These CYPs had similar transcription profiles to that of the known gene responsible for tanshinone production in elicited Danshen hairy roots. Biochemical and RNA interference studies demonstrated that both CYPs are promiscuous. CYP76AH3 oxidizes ferruginol at two different carbon centers, and CYP76AK1 hydroxylates C-20 of two of the resulting intermediates. Together, these convert ferruginol into 11,20-dihydroxy ferruginol and 11,20-dihydroxy sugiol en route to tanshinones. Moreover, we demonstrate the utility of these CYPs by engineering yeast for heterologous production of six oxygenated diterpenoids, which in turn enabled structural characterization of three novel compounds produced by CYP-mediated oxidation.Our results highlight the incorporation of multiple CYPs in diterpenoids metabolic engineering, and a continuing trend of CYPs promiscuity generating complex networks in terpenoid biosynthesis.