scholarly journals Determining Molecular Similarity for Drug Discovery using the Wavelet Riemannian Metric

Author(s):  
Elinor Velasquez ◽  
Emmanuel Yera ◽  
Rahul Singh
2021 ◽  
Author(s):  
Damien Coupry ◽  
Peter Pogany

Abstract Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular similarity based on learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. We also compare the performance of the embedding to standard practices, with a focus on known failure points and edge cases.


Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4723 ◽  
Author(s):  
Javier Vázquez ◽  
Manel López ◽  
Enric Gibert ◽  
Enric Herrero ◽  
F. Javier Luque

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.


Author(s):  
W. Graham Richards

Synopsis:The role of computers in drug discovery depends on just how much is known about the target macromolecule. If atomic detail of the receptor is known, binding free energy differences between drug variants may be computed. Major effort is being expended in extending the area of applicability of such studies by predicting protein structure based on homologies with known protein crystal data. Where no target structure is available, computational methods can provide leads by defining transition state structures and then using the approach of molecular similarity to define stable mimics to act as blockers.


Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1520
Author(s):  
Steven Shave ◽  
Manfred Auer

Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically followed up through purchase and testing of similar compounds, before further funding is sought to begin medicinal chemistry efforts. Rapid exploration of druglike similars and structure–activity relationship profiles can be achieved through our new webservice SimilarityLab. In addition to searching for commercially available molecules similar to a query compound, SimilarityLab also enables the search of compounds with recorded activities, generating consensus counts of activities, which enables target and off-target prediction. In contrast to other online offerings utilizing the USRCAT similarity measure, SimilarityLab’s set of commercially available small molecules is consistently updated, currently containing over 12.7 million unique small molecules, and not relying on published databases which may be many years out of date. This ensures researchers have access to up-to-date chemistries and synthetic processes enabling greater diversity and access to a wider area of commercial chemical space. All source code is available in the SimilarityLab source repository.


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