scholarly journals Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for ‘Orphan’ Molecules

2013 ◽  
Vol 32 (2) ◽  
pp. 133-138 ◽  
Author(s):  
Michael Reutlinger ◽  
Christian P. Koch ◽  
Daniel Reker ◽  
Nickolay Todoroff ◽  
Petra Schneider ◽  
...  
Keyword(s):  
Target Prediction ◽  
Scaffold Hopping

Drug repurposing using similarity-based target prediction, docking studies and scaffold hopping of lefamulin

2020 ◽  
Vol 17 ◽  
Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh
Keyword(s):  
In Silico ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Docking Studies ◽  
Target Protein ◽  
Future Perspective ◽  
Antibacterial Drug ◽  
Scaffold Hopping ◽  
Target Proteins ◽  
Renin Inhibitors

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.

scholarly journals 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping

2016 ◽  
Vol 11 (8) ◽  
pp. 2244-2253 ◽  
Author(s):  
Yu-Chen Lo ◽  
Silvia Senese ◽  
Robert Damoiseaux ◽  
Jorge Z. Torres
Keyword(s):  
Target Prediction ◽  
Scaffold Hopping ◽  
Chemical Similarity ◽  
Similarity Networks

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

Preliminary Study on the Mechanism of Carvacrol Regulating Hepatocellular Carcinoma Based on Network Pharmacology

2019 ◽  
Vol 16 (11) ◽  
pp. 1286-1295
Author(s):  
Sha Li ◽  
Haixia Zhao ◽  
Lidao Bao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Western Blot ◽  
Target Prediction ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Active Compounds ◽  
Prediction Server ◽  
Preliminary Study ◽  
Alignment Analysis ◽  
Endogenous Interactions

Objective: To predict and analyze the target of anti-Hepatocellular Carcinoma (HCC) in the active constituents of Safflower by using network pharmacology. Methods: The active compounds of safflower were collected by TCMSP, TCM-PTD database and literature mining methods. The targets of active compounds were predicted by Swiss Target Prediction server, and the target of anti-HCC drugs was collected by DisGeNET database. The target was subjected to an alignment analysis to screen out Carvacrol, a target of safflower against HCC. The mouse HCC model was established and treated with Carvacrol. The anti-HCC target DAPK1 and PPP2R2A were verified by Western blot and co-immunoprecipitation. Results: A total of 21 safflower active ingredients were predicted. Carvacrol was identified as a possible active ingredient according to the five principles of drug-like medicine. According to Carvacrol's possible targets and possible targets of HCC, three co-targets were identified, including cancer- related are DAPK1 and PPP2R2A. After 20 weeks of Carvacrol treated, Carvacrol group significantly increased on DAPK1 levels and decreased PPP2R2A levels in the model mice by Western blot. Immunoprecipitation confirmed the endogenous interaction between DAPK1 and PPP2R2A. Conclusion: Safflower can regulate the development of HCC through its active component Carvacrol, which can affect the expression of DAPK1 and PPP2R2A proteins, and the endogenous interactions of DAPK1 and PPP2R2A proteins.

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