SmilesDrawer: Parsing and Drawing SMILES-Encoded Molecular Structures Using Client-Side JavaScript

Abstract

Here we present SmilesDrawer, a dependency-free JavaScript component capable of both parsing and drawing SMILES-encoded molecular structures client-side, developed to be easily integrated into web projects and to display organic molecules in large numbers and fast succession. SmilesDrawer can draw structurally and stereochemically complex structures such as maitotoxin and C without using templates, yet has an exceptionally small computational footprint and low memory usage without the requirement for loading images or any other form of client-server communication, making it easy to integrate even in secure (intranet, firewalled) or offline applications. These features allow the rendering of thousands of molecular structure drawings on a single web page within seconds on a wide range of hardware supporting modern browsers. The source code as well as the most recent build of SmilesDrawer is available on Github ( http://doc.gdb.tools/smilesDrawer/ ). Both yarn and npm packages are also available.

Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

“…Molecular structures are drawn from SMILES within the browser using SmilesDrawer (Figure 1d). 51 (Figure 2e). 13 Across the different comparisons above, the simple NN(ECfp4) method, used already in PPB, performed best in terms of recall statistics, which has also been noted by other authors in a related target prediction study.…”

The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

“…Molecular structures are drawn from SMILES within the browser using SmilesDrawer. 44 As an application example we analyzed the case of compound CIS22a, a recently reported TRPV6 calcium channel inhibitor for which we had measured 24 possible off-targets from the "safety screen" panel of Cerep Pvt. Ltd. 34 We compared the prediction of PPB2 with those of SwissTargetPrediction, 29 SEA, 24 Modlab Spider, 28 SuperPred, 30 HitPick, 27 TargetHunter, 25 Chemmapper, 26 TarPred, 31 as well as our previously reported PPB, considering for each tool the top 20 predicted targets ( Figure 3, the structure of CIS22a is shown in Figure 1).…”

MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo-β-Lactamase Inhibitors

“…For the front-end, a framework called 'Materialize' was used in order to design a clean, uniform interface. All SMILES format drawings were dynamically drawn using a JavaScript library (Oracle, Redwood City, CA, USA) named 'SmilesDrawer' that was developed by Daniel Probst and Jean-Louis Reymond [37] (Berne, Switzerland). Any database calls were made by making HTTP requests to an internal PHP Application Programming Interface (API).…”