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

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 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

scholarly journals Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

2015 ◽  
Vol 11 (3) ◽  
pp. e1004153 ◽  
Author(s):  
Yu-Chen Lo ◽  
Silvia Senese ◽  
Chien-Ming Li ◽  
Qiyang Hu ◽  
Yong Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
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.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

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>

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