scholarly journals Compound Screening with Deep Learning for Neglected Diseases: Leishmaniasis

2021 ◽  
Author(s):  
Jonathan Smith ◽  
Hao Xu ◽  
Xinran Li ◽  
Laurence Yang ◽  
Jahir M. Gutierrez

AbstractDeep learning provides a tool for improving screening of candidates for drug re-purposing to treat neglected diseases. We show how a new pipeline can be developed to address the needs of repurposing for Leishmaniasis. In combination with traditional molecular docking techniques, this allows top candidates to be selected and analyzed, including for molecular descriptor similarity.

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>


2020 ◽  
Vol 16 (11) ◽  
pp. 942-948
Author(s):  
Rahila Qureshi ◽  

Leishmaniasis is one of the most neglected diseases with high morbidity and mortality rate. Severe side effects with existing drug and lack of proper vaccine encouraged us to design alternative models to combat the disease. We showed that PP1 of Leishmania donovani mediates immunomodulation in host macrophages needed for parasite survival. Therefore, it is of interest to report the molecular docking analysis of 512 isoflavone derivatives with the phosphatase 1 protein from Leishmania donovani to highlight compound 362 (5-hydroxy-5-{9-[2-methoxy-2-(2-methylfuran-3-yl) ethyl]-1H,3H,4H,10bH-pyrano[4,3-c]chromen-3-yl}pentanoic acid) having good binding features and acceptable ADMET properties for further consideration.


2019 ◽  
Vol 15 (3) ◽  
pp. 240-256 ◽  
Author(s):  
Bianca N.M. Silva ◽  
Policarpo A. Sales Junior ◽  
Alvaro J. Romanha ◽  
Silvane M.F. Murta ◽  
Camilo H.S. Lima ◽  
...  

Background: Chagas disease, also known as American trypanosomiasis, is classified as one of the 17 most important neglected diseases by the World Health Organization. The only drugs with proven efficacy against Chagas disease are benznidazole and nifurtimox, however both show adverse effects, poor clinical efficacy, and development of resistance. For these reasons, the search for new effective chemical entities is a challenge to research groups and the pharmaceutical industry. Objective: Synthesis and evaluation of antitrypanosomal activities of a series of thiosemicarbazones and semicarbazones containing 1,2,3-1H triazole isatin scaffold. Method: 5&'-(4-alkyl/aryl)-1H-1,2,3-triazole-isatins were prepared by Huisgen 1,3-dipolar cycloaddition and the thiosemicarbazones and semicarbazones were obtained by the 1:1 reactions of the carbonylated derivatives with thiosemicarbazide and semicarbazide hydrochloride, respectively, in methanol, using conventional reflux or microwave heating. The compounds were assayed for in vitro trypanocidal activity against Trypanosoma cruzi, the aetiological agent of Chagas disease. Beyond the thio/semicarbazone derivatives, isatin and triazole synthetic intermediates were also evaluated for comparison. Results: A series of compounds were prepared in good yields. Among the 37 compounds evaluated, 18 were found to be active, in particular thiosemicarbazones containing a non-polar saturated alkyl chain (IC50 = 24.1, 38.6, and 83.2 &µM; SI = 11.6, 11.8, and 14.0, respectively). To further elucidate the mechanism of action of these new compounds, the redox behaviour of some active and inactive derivatives was studied by cyclic voltammetry. Molecular docking studies were also performed in two validated protein targets of Trypanosoma cruzi, i.e., cruzipain (CRZ) and phosphodiesterase C (TcrPDEC). Conclusion: A class of thio/semicarbazones structurally simple and easily accessible was synthesized. Compounds containing thiosemicarbazone moieties showed the best results in the series, being more active than the corresponding semicarbazones. Our results indicated that the activity of these compounds does not originate from an oxidation-reduction pathway but probably from the interactions with trypanosomal enzymes.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Nishant Jha ◽  
Deepak Prashar ◽  
Mamoon Rashid ◽  
Mohammad Shafiq ◽  
Razaullah Khan ◽  
...  

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.


2020 ◽  
Vol 11 ◽  
Author(s):  
Liangxu Xie ◽  
Lei Xu ◽  
Ren Kong ◽  
Shan Chang ◽  
Xiaojun Xu

The accurate predicting of physical properties and bioactivity of drug molecules in deep learning depends on how molecules are represented. Many types of molecular descriptors have been developed for quantitative structure-activity/property relationships quantitative structure-activity relationships (QSPR). However, each molecular descriptor is optimized for a specific application with encoding preference. Considering that standalone featurization methods may only cover parts of information of the chemical molecules, we proposed to build the conjoint fingerprint by combining two supplementary fingerprints. The impact of conjoint fingerprint and each standalone fingerprint on predicting performance was systematically evaluated in predicting the logarithm of the partition coefficient (logP) and binding affinity of protein-ligand by using machine learning/deep learning (ML/DL) methods, including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), and deep neural network (DNN). The results demonstrated that the conjoint fingerprint yielded improved predictive performance, even outperforming the consensus model using two standalone fingerprints among four out of five examined methods. Given that the conjoint fingerprint scheme shows easy extensibility and high applicability, we expect that the proposed conjoint scheme would create new opportunities for continuously improving predictive performance of deep learning by harnessing the complementarity of various types of fingerprints.


2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>


2020 ◽  
Author(s):  
Michael F. Cuccarese ◽  
Berton A. Earnshaw ◽  
Katie Heiser ◽  
Ben Fogelson ◽  
Chadwick T. Davis ◽  
...  

ABSTRACTDevelopment of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.


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