scholarly journals Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Guohua Huang ◽  
Yin Lu ◽  
Changhong Lu ◽  
Mingyue Zheng ◽  
Yu-Dong Cai
Keyword(s):  
Drug Development ◽  
Large Scale ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Accuracy Rate ◽  
Computational Approaches ◽  
Starting Point ◽  
Independent Test ◽  
Approved Drugs ◽  
Fold Cross Validation

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

scholarly journals Drug Repositioning for COVID-19

2020 ◽  
Author(s):  
Vicente Benavides-Cordoba
Keyword(s):  
Monoclonal Antibodies ◽  
Drug Development ◽  
Drug Repositioning ◽  
Quality Criteria ◽  
Regulatory Agencies ◽  
Double Blind ◽  
Starting Point ◽  
Potential Activity ◽  
Approved Drugs ◽  
Selection Of

Drug repositioning is a strategy that identifies new uses of approved drugs, to treat conditions different from their original purpose. With the advance of COVID-19 and the declaration of a pandemic; It has become the closest alternative to slow the advance of the virus. Antimalarial, antiviral drugs, antibiotics, glucocorticoids, monoclonal antibodies, among others, are being studied; his findings, although preliminary, could establish a starting point in the search for a solution. In this article, we present a selection of drugs, of different classes and with potential activity to combat COVID-19, whose trials are ongoing; and as proofs of concept, double blind, event-driven add-on, would allow proposing research that generates results in less time and preserving the quality criteria for drug development and approval by regulatory agencies.

scholarly journals Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs

2018 ◽  
Vol 19 (10) ◽  
pp. 3052 ◽  
Author(s):  
Bi Zhao ◽  
Bin Xue
Keyword(s):  
Cross Validation ◽  
Prediction Performance ◽  
Computational Techniques ◽  
Binding Motifs ◽  
Intrinsically Disordered ◽  
Biological Studies ◽  
Independent Test ◽  
Overall Performance ◽  
Improved Accuracy ◽  
Fold Cross Validation

Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for improvement. Among many possibilities, a meta-strategy that integrates the results of multiple individual predictors has been broadly used to improve the overall performance of predictors. Nonetheless, a simple and direct integration of individual predictors may not effectively improve the performance. In this project, dual-threshold two-step significance voting and neural networks were used to integrate the predictive results of four individual predictors, including: DisEMBL, IUPred, VSL2, and ESpritz. The new meta-strategy has improved the prediction performance of intrinsically disordered residues significantly, compared to all four individual predictors and another four recently-designed predictors. The improvement was validated using five-fold cross-validation and in independent test datasets.

scholarly journals Penerapan Algoritma C4.5 Untuk Prediksi Churn Rate Pengguna Jasa Telekomunikasi

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
yohana Tri Utami ◽  
Dewi Asiah Shofiana ◽  
Yunda Heningtyas
Keyword(s):  
Cross Validation ◽  
Primary Reason ◽  
Accuracy Rate ◽  
Customer Churn ◽  
Performance Quality ◽  
Classification Technique ◽  
C4.5 Algorithm ◽  
High Level ◽  
Fold Cross Validation

Telecommunication industries are experiencing substantial problems related to the migration of customers due to a large number of competing companies, dynamic circumstances, as well as the presence of many innovative and attractive offerings. The situation has resulted in a high level of customer migration, affecting a decrement toward the company revenue. Regarding that condition, the customer churn is one well-know approach that can help in increasing the company's revenue and reputation. As to predict the reason behind the migration of customer, this study proposed a data mining classification technique by applying the C4.5 algorithm. Patterns generated by the model were implemented using 10-fold cross-validation, resulting in a model with an accuracy rate of 87%, precision 87.5%, and a recall of 97%. Based on the good performance quality of the model, it can be stated that the C4.5 algorithm succeeded to discover several causes from the migration of telecommunication users, in which price holds the top place as the primary reason

scholarly journals Predictive modeling of religiosity, prosociality, and moralizing in 295,000 individuals from European and non-European populations

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pierre O. Jacquet ◽  
Farid Pazhoohi ◽  
Charles Findling ◽  
Hugo Mell ◽  
Coralie Chevallier ◽  
...  
Keyword(s):  
Sexual Behaviors ◽  
Large Scale ◽  
Cross Validation ◽  
Psychological Explanation ◽  
Collective Actions ◽  
Sample Data ◽  
Out Of Sample ◽  
Prosocial Attitudes ◽  
High Level ◽  
Fold Cross Validation

AbstractWhy do moral religions exist? An influential psychological explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviors through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modeling of data from >295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. First, this methodology reveals no evidence that European and non-European religious people invest more in collective actions and are more trustful of unrelated conspecifics. Instead, the individuals’ level of religiosity is found to be weakly but positively associated with social mistrust and negatively associated with the production of behaviors, which benefit unrelated members of the large-scale community. Second, our models show that individual variation in religiosity is well explained by the interaction of increased levels of social mistrust and increased needs to moralize other people’s sexual behaviors. Finally, stratified k-fold cross-validation demonstrates that the structures of these association patterns are robust to sampling variability and reliable enough to generalize to out-of-sample data.

scholarly journals Computational Drug Repositioning for Chagas Disease Using Protein-Ligand Interaction Profiling

2020 ◽  
Vol 21 (12) ◽  
pp. 4270 ◽  
Author(s):  
Alfredo Juárez-Saldivar ◽  
Michael Schroeder ◽  
Sebastian Salentin ◽  
V. Joachim Haupt ◽  
Emma Saavedra ◽  
...  
Keyword(s):  
Chagas Disease ◽  
Dihydrofolate Reductase ◽  
Drug Repositioning ◽  
Treatment Options ◽  
Ligand Interaction ◽  
Starting Point ◽  
Protein Ligand Interaction ◽  
New Treatment ◽  
Approved Drugs ◽  
Human Dihydrofolate Reductase

Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.

Drug repositioning by prediction of drug’s anatomical therapeutic chemical code via network-based inference approaches

2020 ◽  
Author(s):  
Yayuan Peng ◽  
Manjiong Wang ◽  
Yixiang Xu ◽  
Zengrui Wu ◽  
Jiye Wang ◽  
...  
Keyword(s):  
Cross Validation ◽  
Drug Repositioning ◽  
Anatomical Therapeutic Chemical ◽  
External Validation ◽  
Chemical Properties ◽  
Glucose Deprivation ◽  
Target Drug ◽  
Validation Set ◽  
Fold Cross Validation

Abstract Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug’s anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen–glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.

Identification of FDA Approved Drugs Targeting COVID-19 Virus by Structure-Based Drug Repositioning (Version 2)

2020 ◽  
Author(s):  
Ayman Farag ◽  
Ping Wang ◽  
Mahmoud Ahmed ◽  
Hesham Sadek
Keyword(s):  
Drug Repositioning ◽  
Substrate Binding ◽  
Binding Pocket ◽  
Binding Energies ◽  
Binding Modes ◽  
Substrate Binding Pocket ◽  
Protease Enzyme ◽  
Starting Point ◽  
Approved Drugs ◽  
Fda Approved Drugs

The new strain of Coronaviruses (SARS-CoV-2), and the resulting Covid-19 disease has spread swiftly across the globe after its initial detection in late December 2019 in Wuhan, China, resulting in a pandemic status declaration by WHO within 3 months. Given the heavy toll of this pandemic, researchers are actively testing various strategies including new and repurposed drugs as well as vaccines. In the current brief report, we adopted a repositioning approach using insilico molecular modeling screening using FDA approved drugs with established safety profiles for potential inhibitory effects on Covid-19 virus. We started with structure based drug design by screening more than 2000 FDA approved drugs against Covid-19 virus main protease enzyme (Mpro) substrate-binding pocket focusing on two potential sites (central and terminal sites) to identify potential hits based on their binding energies, binding modes, interacting amino acids, and therapeutic indications. In addition, we elucidate preliminary pharmacophore features for candidates bound to Covid-19 virus Mpro substrate-binding pocket. The top hits bound to the central site of Mpro substrate-binding pocket include antiviral drugs such as Darunavir, Nelfinavir and Saquinavir, some of which are already being tested in Covid-19 patients. Interestingly, one of the most promising hits in our screen is the hypercholesterolemia drug Rosuvastatin. In addition, the top hits bound to the terminal site of Mpro substrate-binding pocket include the anti-asthma drug Montelukast and the anti-histaminic Fexofenadine among others. These results certainly do not confirm or indicate antiviral activity, but can rather be used as a starting point for further in vitro and in vivo testing, either individually or in combination.<br>

scholarly journals Identification of FDA Approved Drugs Targeting COVID-19 Virus by Structure-Based Drug Repositioning

2020 ◽  
Author(s):  
Ayman Farag ◽  
Ping Wang ◽  
Mahmoud Ahmed ◽  
Hesham Sadek
Keyword(s):  
Drug Repositioning ◽  
Binding Energies ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Protease Enzyme ◽  
Starting Point ◽  
Approved Drugs ◽  
Fda Approved Drugs

<div>The new strain of Coronaviruses (SARS-CoV-2), and the resulting Covid-19 disease has spread swiftly across the globe after its initial detection in late December 2019 in Wuhan, China, resulting in a pandemic status declaration by WHO within 3 months. Given the heavy toll of this pandemic, researchers are actively testing various strategies including new and repurposed drugs as well as vaccines. In the current brief report, we adopted a repositioning approach using insilico molecular modeling screening using FDA approved drugs with established safety profiles for potential inhibitory effects on Covid-19 virus. We started with structure based drug design by screening more than 2000 FDA approved drugs</div><div>against Covid-19 virus main protease enzyme (Mpro) substrate-binding pocket to identify potential hits based on their binding energies, binding modes, interacting amino acids, and therapeutic indications. In addition, we elucidate preliminary pharmacophore features for candidates bound to Covid-19 virus Mpro substratebinding pocket. The top hits include anti-viral drugs such as Darunavir, Nelfinavirand Saquinavir, some of which are already being tested in Covid-19 patients. Interestingly, one of the most promising hits in our screen is the hypercholesterolemia drug Rosuvastatin. These results certainly do not confirm or indicate antiviral activity, but can rather be used as a starting point for further in vitro and in vivo testing, either individually or in combination.</div>

scholarly journals Geometric Hashing and ΑΙ-Quantum Deep Learning functional similarities on Remdesivir, drug synergies to treat COVID-19 in Practice.

2021 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Large Scale ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Superior Performance ◽  
Viral Agent ◽  
Innovative Drug ◽  
Modern Drug ◽  
Small Molecule Libraries

Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection. We anticipate that our geometry hashing driven quantum deep learing similarity approaches which is based on separated pairs of short consecutive matching fragments, can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

scholarly journals Drug Repurposing: A Review

2021 ◽  
pp. 161-169
Author(s):  
Rani Teksinh Bhagat ◽  
Santosh Ramarao Butle
Keyword(s):  
Drug Development ◽  
Research And Development ◽  
Development Process ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
New Drugs ◽  
Preclinical Development ◽  
Therapeutic Uses ◽  
Original Application ◽  
Approved Drugs

The drug development is a very time consuming and complex process. Drug development Process is Expensive. Success rate for the new drug development is very small. In recent years, decreases the new drugs development. The powerful tools are developed to support the research and development (R&D) process is essential. The Drug repurposing are helpful for research and development process. The drug re-purposing as an approach finds new therapeutic uses for current candidates or existing candidates or approved drugs, different from its original application. The main aimed of Drug repurposing is to reduce costs and research time investments in Research & Development. It is used for the diagnosis and treatment of various diseases. Repositioning is important over traditional approaches and need for effective therapies. Drug re-purposing identifies new application for already banned or existing drugs from market. In drug design, drug repurposing plays important role, because it helps to preclinical development. It reducing time efforts, expenses and failures in drug discovery process. It is also called as drug repositioning, drug redirecting, drug reprofiling.

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