氯酚化合物对发光细菌毒性的构效关系研究

本文对20种氯酚化合物进行DFT-B3LYP/6-311G**水平全优化计算,据所得量子化学参数构建其对发光细菌毒性的定量构效关系(QSAR)模型。经逐步多元回归分析后,所建立的QSAR模型的相关系数R及去一法(LOO)交互检验复相关系数R2cv分别为0.962和0.876;用预测集样本进行了外部预测,所得外部预测集交互检验Q2ext为0.961,表明所建立的QSAR模型具有较好的稳定性和较强的预测能力。结果表明:分子的体积愈大,化合物毒性愈强;最负非氢原子净电荷愈负,毒性愈强。对模型应用域(AD)进行了表征,所建立的模型可以应用于应用域内氯酚化合物对发光细菌毒性的预测,具有潜在应用价值。     

苯胺类化合物结构与毒性的密度泛函理论研究

应用密度泛函理论(DFT)在杂化泛函B3LYP和基组6-311G(d,p)水平上对57个标题化合物进行结构优化,计算获得化合物的量化和理化参数,采用最佳变量子集回归构建了化合物对梨形四膜虫的毒性与描述符之间的定量结构-活性关系(QSAR)模型。所建最佳三元QSAR模型的复相关系数R2为0.915,留一法(leave-one-out,LOO)交互检验复相关系数Rcv2为0.891;用随机筛选出的19个预测集样本进行外部预测,所得外部预测集交互检验系数Qext2为0.821,QSAR模型具有较好的稳定性和较强的预测能力。模型结果表明影响毒性的主要因素有化合物最低空轨道能、正辛醇-水分配系数及最负非氢原子净电荷。对模型应用域(AD)进行了表征,所建模型可用于应用域内苯胺类化合物对梨形四膜虫毒性的预测。     

含氟农药的比较分子场分析研究

用比较分子场分析(CoMFA)方法对112种含氟农药分子的生物活性及毒性同时进行了定量构效关系研究。用78个化合物作为训练集,以距离比较方法(DISCO)确认的药效团为叠合规则构建CoMFA模型,发现影响活性的立体场与静电场的贡献分别为60.4%和39.6%,影响毒性的立体场与静电场的贡献分别为59.2%和40.8%。药效模型与毒效模型在交叉验证时的相关系数平方(R^2)分别为0.652和0.611,非交叉验证的R^2分别为0.982和0.977,方差比F(8,69)值分别为463.6及362.9,活性和毒性的标准偏差-极差比s/△γ值分别为3.6%和2.9%,表明模型具有较好的自预测能力。对测试组34个化合物进行了活性和毒性的预测,活性与毒性预测的标准偏差-极差比s/△γ值分别为10.4%和6.4%。最后,还建立了一个由97个化合物构建的扩大的模型,各种统计量得到了进一步提高。并预计了一个活性较高且毒性很低的新化合物。     

模型毒物急性毒性尿液核磁共振氢谱标记物的二阶段聚类分析

采用现代核磁共振技术,通过分析给药肝、肾损伤模型化合物异硫氰酸α-萘酯(灌胃150 mg/kg体重)和二溴乙胺氢溴酸盐(腹腔注射250 mg/kg体重)24 h内W istar大鼠尿液的1H NMR谱,由尿液中内源性代谢物浓度变化研究了肝、肾模型毒物在大鼠体内的急性毒性。首次利用模式识别技术中的二阶段聚类分析方法解析大鼠尿液1H NMR谱确定了模型化合物尿液1H NMR标记物。结果表明,应用核磁共振和二阶段聚类分析相结合的方法,可提供模型化合物毒性比较清楚的认识。该方法也可用于金属化合物、中药及其它药物的毒性分类和预测研究以及建议各类靶向毒性的NMR标记物。     

三嗪类化合物溶解度参数及毒性构－效关系

测定了１２种三嗪类化合物的水溶解度，辛醇水分配系数和对发光菌的毒性，并用分子连结性指数建立了预测三嗪类化合物的溶解度，辛醇水分配系数及对发光菌毒性的定量结构活性相关方程，其中１０种化合物文献中未见报道。     

氯代二苯并呋喃(PCDFs)毒性的QSAR研究

从1977年Oile首次报道城市垃圾及工业废弃物中的含氯化合物在焚烧过程中可能产生二噁英以来,人们对二噁英类化合物的产生及其对环境可能造成的危害进行了较为广泛的研究[1-3].氯代二苯并呋喃(PCDFs)是二噁英的一种,一方面由于它具有剧毒性,会导致内分泌系统和体内荷尔蒙平衡紊乱,另一方面它能对机体新陈代谢、免疫力和生殖系统造成长久的损伤,因此,有关此类化合物毒性的研究近年来受到了学术界的关注[4-7].     

三嗪类化合物溶解度参数及毒性构—效关系

测定了１２种三嗪类化合物的水溶解度，辛醇水分配系数和对发光菌的毒性，并用分子连结性指数建立了预测三嗪类化合物的溶解度，辛醇水分配系数及对发光菌毒性的定量结构活性相关方程，其中１０种化合物献中未见报道。e     

模型毒物急性毒性尿液核磁共振氨谱标记物的二阶段聚类分析

采用现代核磁共振技术，通过分析给药肝、肾损伤模型化合物异硫氰酸α-萘酯（灌胃150mg／kg体重）和二溴乙胺氢溴酸盐（腹腔注射250mg／kg体重）24h内Wistar大鼠尿液的^1H NMR谱，由尿液中内源性代谢物浓度变化研究了肝、肾模型毒物在大鼠体内的急性毒性。首次利用模式识别技术中的二阶段聚类分析方法解析大鼠尿液^1H NMR谱确定了模型化合物尿液^1H NMR标记物。结果表明，应用核磁共振和二阶段聚类分析相结合的方法，可提供模型化合物毒性比较清楚的认识。该方法也可用于金属化合物、中药及其它药物的毒性分类和预测研究以及建议各类靶向毒性的NMR标记物。     

分子电性距离矢量用于三嗪类化合物的构效关系研究

采用分子电性距离矢量(Molecular Electronegativity Distance Vector,MEDV)表征了三嗪类化合物的分子结构,并运用多元线性回归(Multiple Linear Regression,MLR)建立了该类化合物结构与其发光菌和大型蚤毒性的定量结构-毒性相关(Quanti-tative Structure-Toxicity Relationship,QSTR)模型,同时采用留一法交互检验对所建模型进行了分析和验证,建模计算值的相关系数R分别为0.970和0.952,留一法交互检验预测值的相关系数RLOO分别为0.917和0.921,并进一步阐述了结构与毒性之间的关系。结果表明,三嗪环上π电子离域程度减小有利于毒性增加,侧链N上取代基数目增加,化合物毒性减小。为进一步预测该类化合物的毒性,进行药物筛选提供了有效的理论依据。     

硝基苯类化合物的FMO位电荷密度能(SHOEi)与生物毒性的QSAR研究

应用简易的量化方法计算了20多种硝基苯衍生物中的64个芳环FMO位电荷密度能SHOEi,用回归法建立一个新的生物毒性评价方程,-lgLC50=0.6191lg Kow+0.1881SHOEi+4.0894,应用所得方程,预测有机物的生物毒性,方程对大多数化合物拟和很好.结果表明,所研究的有机物生物毒性同SHOEi和log Kow密切相关,同化合物与酶的活性点复合或反应是生物中毒的主要因素.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Development and implementation of (Q)SAR modeling within the CHARMMing web‐user interface

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web‐based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing ( www.charmming.org ). This new module implements some of the most recent advances in modern machine learning algorithms—Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. © 2014 Wiley Periodicals, Inc.     

机器学习结合密度泛函理论计算在材料科学中的研究进展

近年来,材料科学研究中密度泛函理论(DFT)计算与机器学习相结合的方法呈现爆炸式增长的趋势。本文综述了DFT及其高通量方法产生的大量计算数据与机器学习相结合的原理和意义,从DFT计算的基本原理出发,重点介绍了机器学习方法的流程、常用的算法及其在催化材料预测热门研究方向中的应用,最后剖析了这个新兴领域目前存在的研究问题、挑战以及未来的发展前景。     

NMR signal processing,prediction, and structure verification with machine learning techniques

Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution.     

Multi-instance multi-label distance metric learning for genome-wide protein function prediction

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms.     

Prediction of biological activity spectra via the Internet

The majority of biologically active compounds have both pharmacotherapeutic and side/toxic actions. To estimate general efficacy and safety of the molecules under study, their biological potential should be thoroughly evaluated. In an early stage of study, only information about structural formulae was available and was used as an input for computational prediction. Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity. An average accuracy of prediction in leave-one-out cross-validation is about 85%. For evaluating new compounds, scientific community may use PASS via the Internet for free at URL: http://www.ibmh.msk.su/PASS. In the first 18 months of PASS Inet's use, approximately 1000 researchers from 60 countries have obtained predicted biological activity spectra for about 23,000 different chemical compounds. More than 64 million PASS predictions for almost 250,000 compounds from Open NCI database are available on the web site http://cactus.nci.nih.gov/ncidb2/. These predictions are used for selecting compounds with desirable and without unwanted types of biological activities among the NCI samples available for screening.     

A semi-supervised learning approach for RNA secondary structure prediction

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.     

Bioinformatics resources and tools for phage display

Databases and computational tools for mimotopes have been an important part of phage display study. Five special databases and eighteen algorithms, programs and web servers and their applications are reviewed in this paper. Although these bioinformatics resources have been widely used to exclude target-unrelated peptides, characterize small molecules-protein interactions and map protein-protein interactions, a lot of problems are still waiting to be solved. With the improvement of these tools, they are expected to serve the phage display community better.