Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation.

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.

InsectiPAD: A Web Tool Dedicated to Exploring Physicochemical Properties and Evaluating Insecticide-Likeness of Small Molecules.

The concept of insecticide-likeness is valuable to select more promising lead candidates during the early stages of drug discovery. We analyzed the physicochemical properties of commercial insecticides and optimized available drug-likeness scoring functions to quantitatively evaluate the insecticidal-like potential of compounds. An interactive platform named Insecticide Physicochemical-properties Analysis Database (InsectiPAD) was developed, which covers 495 approved insecticides and over 22 200 related physicochemical properties. More importantly, it contains over 2900 qualitative analyses and 1500 quantitative scores for insecticides and provides comprehensive insecticide-likeness analysis for any compound. The accuracy and ability of InsectiPAD to distinguish known insecticides (positive sample) from other compounds (negative sample) was also assessed, which is around 75%. Simple input and efficient interpretation are ensured to be useful and helpful for insecticide discovery.

HerbiPAD: a free web platform to comprehensively analyze constitutive property and herbicide-likeness to estimate chemical bioavailability.

BACKGROUND: Herbicides, as efficient weed control measures, play a crucial role in ensuring food security. The emergence of herbicide-resistant weeds has negatively affected food security and promoted the demand for new and improved herbicides. The balance between bioavailability and the potency of a compound is one of the most pressing challenges in the development of novel ideal herbicides. Herbicide-likeness analysis is crucial for the evaluation of this balance and thus may help to address this issue. Many herbicide-likeness analysis methods have been developed to screen potential novel lead compounds. However, there remains a lack of user-friendly and integrated tools to comprehensively evaluate herbicide-likeness. RESULTS: Herbicide-likeness of compounds was assessed through integrated analysis incorporating the physicochemical properties of commercial herbicides, a qualitative rule, and three quantitative scoring functions developed for evaluating herbicide-likeness. HerbiPAD (http://agroda.gzu.edu.cn:9999/ccb/database/HerbiPAD/) is a free web platform integrated with the collected database and scoring model. This platform contains 542 approved herbicides and > 29 000 physicochemical descriptors. The accuracy of HerbiPAD in distinguishing known herbicides from nonherbicides was 84.2%. In the case study, HerbiPAD evaluated 60 new compounds from seven different herbicide targets, and the accuracy of predicting better bioavailability was 83.3%. CONCLUSIONS: HerbiPAD was designed to quickly and efficiently evaluate herbicide-likeness by integrating qualitative and quantitative analyses. The simple and effective interpretation of the analysis interface may help noncomputational experts understand herbicide-likeness. © 2020 Society of Chemical Industry.

A drug-likeness toolbox facilitates ADMET study in drug discovery.

Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research.

Graph attention convolutional neural network model for chemical poisoning of honey bees' prediction.

The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning (DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks (GACNN) with the combination of undirected graph (UG) and attention convolutional neural networks (ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus non-poisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7% Matthews Correlation Coefficient (MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications. In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform (http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.

Molecular Determinants Elucidate the Selectivity in Abscisic Acid Receptor and HAB1 Protein Interactions.

The abscisic acid (ABA), as a pivotal plant hormone, plays a key role in controlling the life cycle and adapting to the environmental stresses. The receptors of ABA are the Pyrabactin resistance/Pyrabactin resistance-like/regulatory component of ABA receptors (PYR/PYL/RCAR, PYLs for simplicity), which regulate the protein phosphatase 2Cs (PP2Cs) in the signal pathway. As an important ABA-mimicking ligand, Pyrabactin shows the activation function to parts of members of PYLs, such as PYR1 and PYL1. Due to the antagonism of Pyrabactin to PYL2, it was used as a probe to discover a part of ABA receptors. Since then, many researchers have been trying to find out the determinants of the selective regulation of PYLs and PP2Cs interaction. However, the roles of residues on the selective regulation of PYR1/PYL2 and PP2Cs interaction induced by Pyrabactin are still ambiguous. This research investigated the selective activation mechanism of Pyrabactin through the sequence alignment, molecular docking, molecular dynamics simulation, and binding free energy calculation. Furthermore, the electrostatic and hydrophobic interaction differences induced by Pyrabactin and agonists were compared. The results indicate that Leu137/Val114, Ser85/Ser89, and Gly86/Gly90 from the pocket and gate of PYR1/PYL2 are the vital residues for the selective activation of Pyrabactin. Meanwhile, the electrostatic interaction between PP2Cs and PYLs complexed with agonists was improved. This mechanism provides strong support for the design of selective agonists and antagonists.

ACID: a free tool for drug repurposing using consensus inverse docking strategy.

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

An efficient synthesis and antifungal evaluation of natural product streptochlorin and its analogues.

Streptochlorin, a small indole alkaloid isolated from marine Streptomyces sp., exhibits a wide range of potent biological activities. An efficient and economic synthetic protocol for streptochlorin has been developed and validated, 4 steps from indole in a total yield of 45%, and further applied for the synthesis of its analogues. Biological testing showed that most of the target compounds exhibited potential antifungal activity in the primary assays, especially compounds 6, 7 and 9c were the most active ones, representing effective activity against the phytopathogenic fungi screened in preliminary test and might be explored for the study of mode of action in the future.

Synthesis and antifungal activity of novel indole-replaced streptochlorin analogues.

Based on examples of the successful applications in drug discovery of bioisosterism, a series of streptochlorin analogues in which indole has been replaced by other heterocycles has been designed and synthesized, as a continuation of our studies aimed at the discovery of novel streptochlorin analogues with improved antifungal activity. Biological testing showed that most of the indole-replaced streptochlorin analogues were inactive, though compound 6f had a broad spectrum of antifungal activity with significant activity against Alternaria solani. The SAR study demonstrated that indole ring is an essential moiety for the antifungal activity of streptochlorin analogues, promoting the idea of indole ring as a framework that might be exploited in the future.

Relationship between brainstem auditory evoked potential and serum neuron-specific enolase in neonates with asphyxia / 中国当代儿科杂志

<p><b>OBJECTIVE</b>To study the correlation between brainstem auditory evoked potential (BAEP) and serum neuron-specific enolase (NSE) in neonates with asphyxia and explore the role of NSE in the evaluation of hearing impairment following asphyxia.</p><p><b>METHODS</b>Fifty-two term neonates with asphyxia, including 38 cases of simple asphyxia (mild: 23 cases; severe: 15 cases) and 14 cases of asphyxia complicated by hypoxic-ischemic encephalopathy (HIE), were enrolled. In the double-blind trial, BAEP and NSE were simultaneously detected 7 days after birth. The patients who did not pass BAEP test received another BAEP and NSE examinations 3 months after birth. Thirty healthy term neonates served as normal control group.</p><p><b>RESULTS</b>Of the 52 neonates with asphyxia, 50.0% and 21.2% of patients failed the initial and the second BAEP tests, respectively. The detection rates of BAEP anomalies in the simple severe asphyxia group in the initial and the second tests (63.3% and 26.3%, respectively) were significantly higher than those in the simple mild asphyxia group (36.9% and 5.9%, respectively)(P<0.05). The neonates with asphyxia complicated by HIE showed a higher detection rate of BAEP anomalies in the second test compared with the asphyxiated neonates without HIE (31.3% vs 16.7%; P<0.05). Mean serum NSE levels in asphyxiated neonates were significantly higher than those in normal controls (<0.01). There were significant differences in serum NSE levels between the neonates with mild and severe asphyxia (26.70+/-2.34 microg/L vs 17.18+/-3.16 microg/L; P<0.01). The asphyxiated neonates with HIE had serum NSE levels similar to the simple severely asphyxiated neonates. Serum NSE levels in patients who failed the initial BAEP test were significantly higher than those who passed the test (25.69+/-4.12 microg/L vs 17.15+/-3.09 microg/L; <0.01). Serum NSE levels had a positive correlation with wave V reaction threshold detected in the BAFP test (<0.05).</p><p><b>CONCLUSIONS</b>The serum level of NSE is closely correlated with BAEP, and it may be useful to the evaluation of the hearing impairment and the outcome in neonates with asphyxia.</p>

Etiology and high risk factors of neonatal ventilator-associated pneumonia / 中国当代儿科杂志

<p><b>OBJECTIVE</b>Ventilator-associated pneumonia (VAP) is a common nosocomial infection and is responsible for a very high mortality in neonatal intensive care unit (NICU) patients. This study was designed to investigate the etiology and high risk factors of neonatal VAP.</p><p><b>METHODS</b>The clinical data of 106 critical neonates who were treated with mechanical ventilator between 2003 and 2005 were studied retrospectively.</p><p><b>RESULTS</b>Of the 106 neonates, 84 received mechanical ventilation for > or = 48 hrs. Thirty-five (41.7%) out of the 84 patients developed VAP. Univariate analysis showed that gestational age, duration of mechanical ventilation, reintubation, birth weights, primary lung disease and gamma globulin administration were associated with the development of VAP (P < 0.05). Multivariate stepwise logistic regression analysis showed that primary lung disease (OR=3.671, 95% CI=1.0-13.45, P < 0.05), duration of mechanical ventilation (OR=4.945, CI=1.51-16.21, P < 0.01), reintubation (OR=7.721, 95% CI=2.31-25.85, P < 0.01) and high-dose gamma globulin administration (OR=5.520, 95%CI=2.08-16.26, P < 0.01) were predicted factors for the development of VAP. The detection rate of gram negative bacilli (76.9%) was the highest, followed by gram positive coccus (17.9%) in VAP patients.</p><p><b>CONCLUSIONS</b>Opportunistic drug-resistant bacteria are common pathogens for neonatal VAP. The risk of VAP is multifactorial, including external medical environments and patients' internal agents.</p>