Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition

The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.

ESTIMATION OF STANDARD ENTHALPY OF FORMATION OF ALKANES IN GASEOUS STATE BY CALCULATING SIZE, STRUCTURAL AND ELECTRONIC PARAMETERS IN THE MOLECULES

A quantitative analysis is made on the correlation ship of thermodynamic property, i.e., standard enthalpy of formation (ΔH f0) with Kier’s molecular connectivity index(1Xv),vander waal’s volume (Vw) electrotopological state index (E) and refractotopological state index (R) in gaseous state of alkanes. The regression analysis reveals a significant linear correlation of standard enthalpy of formation (ΔH f0) with 1Xv, Vw, E and R. The equations obtained by regression analysis may be used to estimate standard enthalpy of formation (ΔH f0) of alkanes in gaseous state.

Exploring QSAR of Some Antitubercular Agents

The in vitro vero cell cytotoxicity of 93 antitubercular compounds belonging to the classes of chiral pentaamines, bis-cyclic guanidines, bis-cyclic thioureas, bis-cyclic piperazines, and quinolylhydrazones has been modeled in the present quantitative structure-activity relationship (QSAR) study. Genetic function approximation followed by multiple linear regression (GFA-MLR) based on the mean absolute error (MAE) based criteria was used as the chemometric tool for the model development using 2D descriptors available from open source PaDEL-Descriptor. The developed model was statistically robust (Q2:0.868, R2pred:0.896). Additionally, the r2m metrics, concordance correlation coefficient (CCC) and MAE criteria for the test set validation were also tested. The models indicate importance of autocorrelation descriptors weighted by charge (ATSc3, ATSc5) and some electrotopological state atom type descriptor of fragments -NH-,-O-, >N- for cytotoxicity. The applicability domains of GFA-MLR models were also studied by applying both leverage and standardized residual approaches.

Research on Neural Network Theory of the Toxicity of Aromatic Hydrocarbons on Chlorella vulgaris

Using BP neural network method, we calculate and analyze the molecular structure of aromatic hydrocarbons. Then, we get the electrotopological state indices and the molecular electronegativity distance vectors of 25 aromatic hydrocarbons based on the calculation of molecular structure characteristics and adjacency matrix. By regression, we get and optimize the structural parametersE9,E13,E17andM15. The four structural parameters are used as the input variables and a 4-2-1 network structure is employed to construct a BP artificial neural network model for predicting acute toxicitypEC50. The total correlation coefficientRis 0.994 and the average error between the predicted value and experimental value ofpEC50is 0.079, which indicate that the ANN model has good stability and superior predictive ability. The results show that there is a good nonlinear correlation between acute toxicitypEC50and the four structural parameters. The results of our research reveal that the toxicity of aromatic hydrocarbons is closely affected by electrotopological state indices and the molecular electronegativity distance vectors. Therefore, it will be helpful in assessing the hazard of aromatic hydrocarbons to environment.

Graph-Theoretical Indices based on Simple, General and Complete Graphs

Valence molecular connectivity indices are based on the concept of valence delta, d v, that can be derived from general chemical graphs or chemical pseudographs. A general graph or pseudograph has multiple edges and loops and can be used to encode, through the valence delta, chemical entities. Two graph-theoretical concepts derived from chemical pseudographs are the intrinsic (I) and the electrotopological state (E) values, which are the used to define the valence delta of the pseudoconnectivity indices, ?I,S. Complete graphs encode, through a new valence delta, the core electrons of any atoms in a molecule. The connectivity indices, either valence connectivity or pseudoconnectivity, are the starting point to develop the dual connectivity indices. The dual indices show that not only can they assume negative values but also cover a wide range of numerical values. The central parameter of the molecular connectivity theory, the valence delta, defines a completely new set of connectivity indices, which can be distinguished by their configuration and advantageously used to model different properties and activities of compounds.