scholarly journals Chemical Similarity Networks for Drug Discovery

10.5772/65106 ◽  
2016 ◽  
Author(s):  
Yu-Chen Lo ◽  
Jorge Z. Torres
Keyword(s):  
Drug Discovery ◽  
Chemical Similarity ◽  
Similarity Networks

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

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

scholarly journals Chemoinformatic-guided engineering of polyketide synthases

2019 ◽  
Author(s):  
Amin Zargar ◽  
Ravi Lal ◽  
Luis Valencia ◽  
Jessica Wang ◽  
Tyler William H. Backman ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Drug Discovery ◽  
Polyketide Synthase ◽  
Short Chain Fatty Acids ◽  
Polyketide Synthases ◽  
Chemical Similarity ◽  
Atom Pair ◽  
Genetic Origin ◽  
Significant Challenge ◽  
Streptomyces Albus

AbstractPolyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge in PKS design is engineering a partially reductive module to produce a saturated β-carbon through a reductive loop exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, could provide a viable strategy to reductive loop exchanges. We first introduced a set of donor reductive loops of diverse genetic origin and chemical substrate structures into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with atom pair chemical similarity between the substrate of the donor reductive loops and recipient LipPKS1, reaching a titer of 165 mg/L of short chain fatty acids produced by Streptomyces albus J1074 harboring these engineered PKSs. Expanding this method to larger intermediates requiring bimodular communication, we introduced reductive loops of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.

scholarly journals Accelerating Drug Discovery and Repurposing by Combining Transcriptional Signature Connectivity with Docking

2020 ◽  
Author(s):  
Alexander W. Thorman ◽  
James Reigle ◽  
Somchai Chutipongtanate ◽  
Behrouz Shamsaei ◽  
Marcin Pilarczyk ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
False Positive ◽  
Target Gene ◽  
Treatment Options ◽  
Small Subset ◽  
Chemical Similarity ◽  
Docking Simulations ◽  
Transcriptional Signature

AbstractThe development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their ‘concordant’ LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude.

Study of the Similarity Between Molecules Through the Comparison of Thermo-chemical Properties

2018 ◽  
Author(s):  
Tetsafe De Angeli
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Chemical Properties ◽  
Quantitative Structure Activity Relationship ◽  
Computational Science ◽  
Thermochemical Properties ◽  
Quantitative Structure ◽  
Chemical Similarity ◽  
Chemical Fingerprints ◽  
Structure Activity

Chemical similarity between molecules, is a key concept in drug design and drug discovery. The advance in computational science, had given rise to many new possibilities for understand difference and similarity between molecules. In particular, is very important the QSAR (quantitative structure–activity relationship ) paradigm (1). Typical approaches to calculate chemical similarities use chemical fingerprints or QSAR, but this doesn´t consider the thermochemical properties of the molecules. In others words chemical similarity is described as an inverse of a measure of distance in descriptor space. In this work is presented a new method, for calculate chemical similarities

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