有机磷农药构效关系的主成分分析-人工神经网络研究

采用主成分分析法对样本数据集进行预处理，将得到的新的样本数据集输入人工神经网络，对有机磷农药的毒性参数进行预报。研究结果表明，主成分分析－人工神经网络的预报精度优于单纯的人工神经网络。     

The Non-Grid Technique for Modeling 3D QSAR Using Self-Organizing Neural Network (SOM) and PLS Analysis: Application to Steroids and Colchicinoids

Abstract

A novel method for modeling 3D QSAR has been developed. The method involves a multiple training of a series of self-organizing networks (SOM). The obtained networks have been used for processing the data of one reference molecule. A scheme for the analysis of such data with the PLS analysis has been proposed and tested using the steroids data with corticosteroid binding globulin (CBG) affinity. The predictivity of the CBG models measured with the SDEP parameter is among the best one reported. Although 3-D QSAR models for colchicinoid series is far less predictive, it allows for a discussion on the relative influence of the structural motifs of these compounds.     

On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research

Abstract

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.     

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.     

Electron Transfer and Charge Transfer: Twin Themes in Unifying the Mechanisms of Organic and Organometallic Reactions

The broad varieties of organic and organometallic reactions merge into a common unifying mechanism by considering all nucleophiles and electrophiles as electron donors (D) and electron acceptors (A), respectively. Comparison of outer-sphere and inner-sphere electron transfers with the aid of Marcus theory provides the thermochemical basis for the generalized free energy relationship for electron transfer (FERET) in Equation (37) and its corollaries in Equations (43) and (44) that have wide predictive applicability to electrophilic aromatic substitutions, olefin additions, organometallic cleavages, etc. The FERET is based on the conversion of the weak nucleophile–electrophile interactions extant in the ubiquitous electron donor—acceptor (EDA) precursor complex [D, A] to the radical ion pair [D^⊕, A^?], for which the free energy change can be evaluated from the charge-transfer absorption spectra according to Mulliken theory. FERET analysis thus indicates that the charge-transfer ion pairs [D^⊕, A^?] are energetically equivalent to the transition states for nucleophile/electrophile transformations. The behavior of such ion pairs can be directly observed immediately following the irradiation of the charge-transfer bands of various EDA complexes with a 25-ps laser pulse. Such studies confirm the radical ion pair [Arene^⊕, NO₂] as a viable intermediate in electrophilic aromatic nitration, as presented in the electron-transfer mechanism between arenes and the nitryl cation (NO) electrophile.     

Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals

There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantially increases the potentialities of this kind of networks, avoiding time and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one species. To solve this problem we developed a multispecies QSAR classification model, in which the outputs were the inputs of the aforementioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extend model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power law network with few mechanisms (network hubs).     

预测金属有机骨架甲烷和氢气输送能力的迁移学习建模

基于深度神经网络(DNN)和迁移学习(TL),使用少量数据建立TL模型,精准预测了金属有机骨架(MOFs)的甲烷和氢气输送性能.首先,使用8414个MOFs在298 K/65 bar～298 K/5.8 bar(1 bar=0.1 MPa)条件下的甲烷输送数据训练一个决定系数(R²)为0.973的DNN[源任务(ST)模型].随后,将ST模型的部分参数冻结,使用100个MOFs在233 K/65 bar～358 K/5.8 bar条件下的甲烷输送数据和100个MOFs在198 K/100 bar～298 K/5 bar条件下的氢气输送数据分别微调ST模型,进行TL建模.结果表明,两个TL模型的R²分别为0.968和0.945,均高于其它5个传统的ML模型.所开发的TL模型在预测小数据集时具有高精度与高稳定性.最后,使用排列特征重要度方法来计算描述符重要度,明确了模型之间的“知识”共享情况,并在此基础上探讨了重要描述符和输送能力之间的关系.     

Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiO2 and Heavy Metals: QSAR Model Apply Random Forest Algorithm after Clustering Analysis

With the development and application of nanomaterials, their impact on the environment and organisms has attracted attention. As a common nanomaterial, nano-titanium dioxide (nano-TiO₂) has adsorption properties to heavy metals in the environment. Quantitative structure-activity relationship (QSAR) is often used to predict the cytotoxicity of a single substance. However, there is little research on the toxicity of interaction between nanomaterials and other substances. In this study, we exposed human renal cortex proximal tubule epithelial (HK-2) cells to mixtures of eight heavy metals with nano-TiO₂, measured absorbance values by CCK-8, and calculated cell viability. PLS and two ensemble learning algorithms are used to build multiple QSAR models for data sets, and the test set R² is increased from 0.38 to 0.78 and 0.85, and RMSE is decreased from 0.18 to 0.12 and 0.10. After selecting the better random forest algorithm, the K-means clustering algorithm is used to continue to optimize the model, increasing the test set R² to 0.95 and decreasing the RMSE to 0.08 and 0.06. As a reliable machine algorithm, random forest can be used to predict the toxicity of the mixture of nano-metal oxides and heavy metals. The cluster analysis can effectively improve the stability and predictability of the model, and provide a new idea for the prediction of cytotoxicity model in the future.     

高中化学竞赛有机部分的学习方法和技巧

全国高中化学竞赛中,有机化学一直是竞赛内容的重要部分,其难度较大。因此,结合近年来全国高中化学竞赛试题阐述了基础有机化学反应、有机化学理论知识和有机化学反应历程3个模块的学习方法;总结了归纳法、逆推法、综合分析法等解答有机试题的技巧,从而帮助学生短时间内提高竞赛成绩。     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

Influence of Intermolecular Vibrations on the Electronic Coupling in Organic Semiconductors: The Case of Anthracene and Perfluoropentacene

We have performed classical molecular dynamics simulations and quantum‐chemical calculations on molecular crystals of anthracene and perfluoropentacene. Our goal is to characterize the amplitudes of the room‐temperature molecular displacements and the corresponding thermal fluctuations in electronic transfer integrals, which constitute a key parameter for charge transport in organic semiconductors. Our calculations show that the thermal fluctuations lead to Gaussian‐like distributions of the transfer integrals centered around the values obtained for the equilibrium crystal geometry. The calculated distributions have been plugged into Monte‐Carlo simulations of hopping transport, which show that lattice vibrations impact charge transport properties to various degrees depending on the actual crystal structure.     

配体-受体相互作用的计算机模拟及其在药物设计中的应用

配体-受体相互作用在许多生物过程中起重要作用, 计算机分子模拟技术和理论化学计算方法在配体-受体相互作用研究中得到了广泛的应用。本文综述了有关配体-受体相互作用分子模拟和理论计算的常用方法及其在药物设计中的应用。     

Allosteric Control of Naphthalene Diimide Encapsulation and Electron Transfer in Porphyrin Containers: Photophysical Studies and Molecular Dynamics Simulation

The complexation processes of N,N’-dibutyl-1,4,5,8-naphthalene diimide ( NDI ) into two types of π-electron-rich molecular containers consisting of two Zn(II)-porphyrins connected by four flexible linkers of two different lengths, were characterized by means of absorption and emission spectroscopies and molecular dynamics simulation. Notably, the addition of NDI leads to a strong quenching of the fluorescence of both cages only when they are in an open conformation suitable for guest encapsulation, a situation triggered by silver(I) ions binding to the lateral triazoles. Molecular dynamics simulations confirm the fast binding of NDI , likely assisted by NDI -silver(I) interactions. Upon NDI complexation, the two porphyrin macrocycles get closer, with an optimized face to face orientation, suggesting an induced-fit mechanism through π–π interactions with the NDI aromatic cycle. Ultrafast transient absorption experiments allowed to identify the process of quenching of the Zn-porphyrin fluorescence as an efficient photoinduced electron transfer reaction between the cage porphyrin and the included NDI guest. The process occurs on fast and ultrafast time scales in the two complexes (1.5 ps and ≤300 fs) leading to a short-lived charge separated state (charge recombination lifetimes in the order of 30–40 ps). The combined computational and experimental approach used here is able to furnish a reliable model of the NDI -cage complexation mechanism and of the corresponding electron transfer reaction, attesting the allosteric control of both processes by the silver(I) ions.     

Exploring Natural Product Activity and Species Source Candidates for Hunting ABCB1 Transporter Inhibitors: An In Silico Drug Discovery Study

The P-glycoprotein (P-gp/ABCB1) is responsible for a xenobiotic efflux pump that shackles intracellular drug accumulation. Additionally, it is included in the dud of considerable antiviral and anticancer chemotherapies because of the multidrug resistance (MDR) phenomenon. In the search for prospective anticancer drugs that inhibit the ABCB1 transporter, the Natural Product Activity and Species Source (NPASS) database, containing >35,000 molecules, was explored for identifying ABCB1 inhibitors. The performance of AutoDock4.2.6 software to anticipate ABCB1 docking score and pose was first assessed according to available experimental data. The docking scores of the NPASS molecules were predicted against the ABCB1 transporter. Molecular dynamics (MD) simulations were conducted for molecules with docking scores lower than taxol, a reference inhibitor, pursued by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations. On the basis of MM-GBSA calculations, five compounds revealed promising binding affinities as ABCB1 inhibitors with ΔG_binding < −105.0 kcal/mol. The binding affinity and stability of the identified inhibitors were compared to the chemotherapeutic agent. Structural and energetical analyses unveiled great steadiness of the investigated inhibitors within the ABCB1 active site throughout 100 ns MD simulations. Conclusively, these findings point out that NPC104372, NPC475164, NPC2313, NPC197736, and NPC477344 hold guarantees as potential ABCB1 drug candidates and warrant further in vitro/in vivo tests.     

Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches

Prostate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to investigate how obesity influences the noncutaneous malignancies of PCa. By comparing the core signaling pathways of lean and obese patients with PCa, we are able to investigate the relationships between obesity and pathogenic mechanisms and identify significant biomarkers as drug targets for drug discovery. Regarding drug design specifications, we take drug–target interaction, drug regulation ability, and drug toxicity into account. One deep neural network (DNN)-based drug–target interaction (DTI) model is trained in advance for predicting drug candidates based on the identified biomarkers. In terms of the application of the DNN-based DTI model and the consideration of drug design specifications, we suggest two potential multiple-molecule drugs to prevent PCa (covering lean and obese PCa) and obesity-specific PCa, respectively. The proposed multiple-molecule drugs (apigenin, digoxin, and orlistat) not only help to prevent PCa, suppressing malignant metastasis, but also result in lower production of fatty acids and cholesterol, especially for obesity-specific PCa.     

BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules

Recent advances in artificial intelligence along with the development of large data sets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far require the atomic positions obtained from geometry optimizations using high-level QM/DFT methods as input in order to predict the energies and do not allow for geometry optimization. In this study, a transferable and molecule size-independent machine learning model bonds (B), angles (A), nonbonded (N) interactions, and dihedrals (D) neural network (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and nonequilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N), and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational, and reaction space. The transferability of this model on systems larger than the ones in the data set is demonstrated by performing calculations on selected large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from nonequilibrium structures along with predicting their energies. © 2019 Wiley Periodicals, Inc.