scholarly journals Quantitative Prediction Model for Affinity of Drug-Target Interactions Based on Molecular Vibration and Overall System of Ligand-Receptor

2021 ◽  
Author(s):  
xian rui wang ◽  
ting ting cao ◽  
cong min jia ◽  
xue mei tian ◽  
Yun Wang
Keyword(s):  
Drug Target ◽  
Prediction Models ◽  
Molecular Descriptors ◽  
Quantitative Prediction ◽  
Molecular Vibrations ◽  
Optimal Model ◽  
Comprehensive Comparison ◽  
Test Sets ◽  
Evaluation Parameters ◽  
Selection Of

Abstract Background: the study of drug-target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure-activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets and most QSAR and MD were based either only on structure of drug molecules or on structure of targets with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy with wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction model with high accuracy-wide applicability based on Kd and EC50, and to provide reference for quantifying affinity of DTIs.Methods: Through parametric characterization based on molecular vibrations and protein sequences, taking molecule-target as whole system and feature selection of drug molecule-target, we constructed feature datasets of DTIs quantified by Kd and EC50, respectively. Then, prediction models were constructed using above datasets and SVM, RF and ANN. In addition, optimal models were selected for application evaluation and comprehensive comparison.Results: Under ten-fold cross-validation, evaluation parameters based on RF for EC50 dataset are as follows: R2 (RF) of training and test sets are 0.9611, 0.9641; MSE (RF) of training and test sets are 0.0891, 0.0817. Evaluation parameters based on RF for Kd dataset are as follows: R2 (RF) of training and test sets are 0.9425, 0.9485; MSE (RF) of training and test sets are 0.1208, 0.1191. After comprehensive comparison, the results showed that RF model in this paper is optimal model. In application evaluation of RF model, the errors of most prediction results were in range of 1.5-2.0.Conclusion: Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal model based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of model. It can provide reference for quantifying affinity of DTIs.

scholarly journals Quantitative prediction model for affinity of drug–target interactions based on molecular vibrations and overall system of ligand-receptor

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xian-rui Wang ◽  
Ting-ting Cao ◽  
Cong Min Jia ◽  
Xue-mei Tian ◽  
Yun Wang
Keyword(s):  
Drug Target ◽  
Prediction Models ◽  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
High Accuracy ◽  
Quantitative Prediction ◽  
Maximal Effect ◽  
Molecular Vibrations ◽  
Wide Applicability ◽  
Drug Molecules

Abstract Background The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs. Results After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R2) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs. Conclusion Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

scholarly journals A predictable smoothing evolution model for computer-controlled polishing

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Jing Hou ◽  
Pengli Lei ◽  
Shiwei Liu ◽  
Xianhua Chen ◽  
Jian Wang ◽  
...  
Keyword(s):  
Evolution Model ◽  
Quantitative Prediction ◽  
Theoretical Support ◽  
Model Combining ◽  
Process Guidance ◽  
Dependent Model ◽  
Computer Controlled Optical Surfacing ◽  
Computer Controlled ◽  
Time Dependent Model ◽  
Selection Of

AbstractQuantitative prediction of the smoothing of mid-spatial frequency errors (MSFE) is urgently needed to realize process guidance for computer controlled optical surfacing (CCOS) rather than a qualitative analysis of the processing results. Consequently, a predictable time-dependent model combining process parameters and an error decreasing factor (EDF) were presented in this paper. The basic smoothing theory, solution method and modification of this model were expounded separately and verified by experiments. The experimental results show that the theoretical predicted curve agrees well with the actual smoothing effect. The smoothing evolution model provides certain theoretical support and guidance for the quantitative prediction and parameter selection of the smoothing of MSFE.

scholarly journals Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elisabeth J. Schiessler ◽  
Tim Würger ◽  
Sviatlana V. Lamaka ◽  
Robert H. Meißner ◽  
Christian J. Cyron ◽  
...  
Keyword(s):  
Corrosion Inhibition ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Automatic Identification ◽  
Organic Additives ◽  
Statistical Tool ◽  
Degradation Behaviour ◽  
Corrosion Inhibition Efficiency ◽  
Selection Of

AbstractThe degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

SOMES: A REVIEW ON COMPOSITION, FORMULATION METHODS AND EVALUATIONS OF DIFFERENT TYPES OF “SOMES” DRUG DELIVERY SYSTEM

2020 ◽  
pp. 7-18
Author(s):  
KUSUMA PRIYA M. D. ◽  
VINOD KUMAR ◽  
DAMINI V. K. ◽  
ESWAR K. ◽  
KADIRI RAJESH REDDY ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Sustained Release ◽  
Surface Chemistry ◽  
Drug Delivery System ◽  
Delivery System ◽  
Different Types ◽  
Potent Drug ◽  
Evaluation Parameters ◽  
Selection Of

Many drugs are available in the market for several diseases, disorder or even for a condition, but it is difficult to select a suitable carrier to attain maximum bioavailability and potential for a potent drug. Attaining a controlled and sustained release of a drug is purely focused on the selection of a carrier (natural, synthetic and hybrid) like nanosomes. Nanosomes have become a prominent tool in the field of pharmacy. Nanosomes are small uniform structures which deliver the drug to the specific targeted site, which mainly depends upon the presence of ligands, shape, size and surface chemistry. Nanosomes are available in various types which include Niosomes, Liposomes, Electrosomes, Aquasomes, Transfersomes, Phytosomes, Enzymosomes, Ethosomes, Invasome and Sphingosomes. In general, all these nanosomes are quite similar in nature with minute differences in their vesicular characteristics and composition. This review traces various ‘somes’ composition and their role in the formulation, applications, advantages, disadvantages, common formulation procedures and evaluation parameters.

scholarly journals DeepPurpose: a deep learning library for drug–target interaction prediction

2020 ◽  
Author(s):  
Kexin Huang ◽  
Tianfan Fu ◽  
Lucas M Glass ◽  
Marinka Zitnik ◽  
Cao Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Prediction Models ◽  
State Of The Art ◽  
Supplementary Information ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Computer Scientists ◽  
Benchmark Datasets ◽  
Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Computational Model Development of Drug-Target Interaction Prediction: A Review

2019 ◽  
Vol 20 (6) ◽  
pp. 492-494 ◽  
Author(s):  
Qi Zhao ◽  
Haifan Yu ◽  
Mingxuan Ji ◽  
Yan Zhao ◽  
Xing Chen
Keyword(s):  
Drug Target ◽  
Prediction Models ◽  
Model Development ◽  
Computational Prediction ◽  
Medical Field ◽  
Research Directions ◽  
Target Interaction ◽  
Protein Target ◽  
Interaction Prediction ◽  
The Past

In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.

scholarly journals Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands

2016 ◽  
Vol 9 (7) ◽  
pp. 3429-3454 ◽  
Author(s):  
Satoshi Takahama ◽  
Giulia Ruggeri ◽  
Ann M. Dillner
Keyword(s):  
Infrared Spectra ◽  
Atmospheric Aerosols ◽  
Quantitative Prediction ◽  
Calibration Model ◽  
Vibrational Modes ◽  
Aerosol Composition ◽  
Absorption Bands ◽  
Calibration Models ◽  
Ft Ir ◽  
Selection Of

Abstract. Various vibrational modes present in molecular mixtures of laboratory and atmospheric aerosols give rise to complex Fourier transform infrared (FT-IR) absorption spectra. Such spectra can be chemically informative, but they often require sophisticated algorithms for quantitative characterization of aerosol composition. Naïve statistical calibration models developed for quantification employ the full suite of wavenumbers available from a set of spectra, leading to loss of mechanistic interpretation between chemical composition and the resulting changes in absorption patterns that underpin their predictive capability. Using sparse representations of the same set of spectra, alternative calibration models can be built in which only a select group of absorption bands are used to make quantitative prediction of various aerosol properties. Such models are desirable as they allow us to relate predicted properties to their underlying molecular structure. In this work, we present an evaluation of four algorithms for achieving sparsity in FT-IR spectroscopy calibration models. Sparse calibration models exclude unnecessary wavenumbers from infrared spectra during the model building process, permitting identification and evaluation of the most relevant vibrational modes of molecules in complex aerosol mixtures required to make quantitative predictions of various measures of aerosol composition. We study two types of models: one which predicts alcohol COH, carboxylic COH, alkane CH, and carbonyl CO functional group (FG) abundances in ambient samples based on laboratory calibration standards and another which predicts thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) mass in new ambient samples by direct calibration of infrared spectra to a set of ambient samples reserved for calibration. We describe the development and selection of each calibration model and evaluate the effect of sparsity on prediction performance. Finally, we ascribe interpretation to absorption bands used in quantitative prediction of FGs and TOR OC and EC concentrations.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
Author(s):  
Wen Zhang ◽  
Weiran Lin ◽  
Ding Zhang ◽  
Siman Wang ◽  
Jingwen Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Prediction Models ◽  
Prediction Methods ◽  
Crucial Issue ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Recent Advances ◽  
Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

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