scholarly journals Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0

2017 ◽  
Author(s):  
Mathilde Koch ◽  
Thomas Duigou ◽  
Pablo Carbonell ◽  
Jean-Loup Faulon
Keyword(s):  
Virtual Screening ◽  
Open Source ◽  
Chemical Space ◽  
Enzymatic Reaction ◽  
Molecular Structures ◽  
Use Cases ◽  
Structural Isomers ◽  
E Coli ◽  
Synthesis Planning ◽  
Structure Enumeration

AbstractBackgroundNetwork generation tools coupled with chemical reaction rules have been mainly developed for synthesis planning and more recently for metabolic engineering. Using the same core algorithm, these tools apply a set of rules to a source set of compounds, stopping when a sink set of compounds has been produced. When using the appropriate sink, source and rules, this core algorithm can be used for a variety of applications beyond those it has been developed for.ResultsHere, we showcase the use of the open source workflow RetroPath2.0. First, we mathematically prove that we can generate all structural isomers of a molecule using a reduced set of reaction rules. We then use this enumeration strategy to screen the chemical space around a set of monomers and predict their glass transition temperatures, as well as around aminoglycosides to search structures maximizing antibacterial activity. We also perform a screening around aminoglycosides with enzymatic reaction rules to ensure biosynthetic accessibility. We finally use our workflow on an E. coli model to complete E. coli metabolome, with novel molecules generated using promiscuous enzymatic reaction rules. These novel molecules are searched on the MS spectra of an E. coli cell lysate interfacing our workflow with OpenMS through the KNIME analytics platform.ConclusionWe provide an easy to use and modify, modular, and open-source workflow. We demonstrate its versatility through a variety of use cases including, molecular structure enumeration, virtual screening in the chemical space, and metabolome completion. Because it is open source and freely available on MyExperiment.org, workflow community contributions should likely expand further the features of the tool, even beyond the use cases presented in the paper.

scholarly journals Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Mathilde Koch ◽  
Thomas Duigou ◽  
Pablo Carbonell ◽  
Jean-Loup Faulon
Keyword(s):  
Virtual Screening ◽  
Chemical Space ◽  
Molecular Structures

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Industrie-4.0-Middleware geht in die Anwendung/Industrie 4.0 middleware enters the application stage

2021 ◽  
Vol 111 (06) ◽  
pp. 446-448
Author(s):  
Thomas Kuhn
Keyword(s):  
Open Source ◽  
Industrie 4.0 ◽  
Use Cases ◽  
Basic System ◽  
Research Project ◽  
Industrial Companies

In dem Forschungsprojekt „Basissystem für die unternehmensübergreifende Produktionsunterstützung“ (kurz: BaSys überProd) arbeiten 21 Partner aus Wissenschaft und Wirtschaft an Lösungen für den Wandel hin zur digitalisierten, flexiblen Industrie-4.0-Produktion. Dabei kommt vor allem die Open-Source-Middleware Eclipse BaSyx zum Einsatz. Das Ziel: Repräsentative Anwendungsfälle in Wirtschaftsunternehmen umsetzen und das Wiederverwendungspotenzial der Lösungen für andere Kontexte herausarbeiten.   In the research project “Basissystem für die unternehmensübergreifende Produktionsunterstützung” (Basic system for cross-company production support; BaSys überProd for short), 21 partners from research and industry are working on solutions for the transition to digitalized, flexible Industrie 4.0 production. In this context, the open-source middleware Eclipse BaSyx, in particular, is being used. The goal is to implement representative use cases in industrial companies and to identify the reuse potential of the solutions for other contexts.

From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space

2011 ◽  
Vol 31 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Alexander Klenner ◽  
Volker Hähnke ◽  
Tim Geppert ◽  
Petra Schneider ◽  
Heiko Zettl ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Bioactive Compounds ◽  
Chemical Space

Characterization of Thermophilic Bacteria Using Surface-Enhanced Raman Scattering

2008 ◽  
Vol 62 (11) ◽  
pp. 1226-1232 ◽  
Author(s):  
Mustafa Çulha ◽  
Ahmet Adigüzel ◽  
M. MÜGE Yazici ◽  
Mehmet Kahraman ◽  
Fikrettin Slahin ◽  
...  
Keyword(s):  
Cell Wall ◽  
Raman Scattering ◽  
Elevated Temperatures ◽  
Thermophilic Bacteria ◽  
Surface Enhanced Raman Scattering ◽  
Molecular Structures ◽  
E Coli ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) can provide molecular-level information about the molecules and molecular structures in the vicinity of nanostructured noble metal surfaces such as gold and silver. The three thermophilic bacteria Bacillus licheniformis, Geobacillus stearothermophilus, and Geobacillus pallidus, a Gram-negative bacterium E. coli, and a Gram-positive bacterium B. megaterium are comparatively characterized using SERS. The SERS spectra of thermophilic bacteria are similar, while they show significant differences compared to E. coli and B. megaterium. The findings indicate that a higher number of thiol residues and possible S–S bridges are present in the cell wall structure of thermophilic bacteria, providing their stability at elevated temperatures. Incubating the thermophilic bacteria with colloidal silver suspension at longer times improved the bacteria–silver nanoparticle interaction kinetics, while increased temperature does not have a pronounced effect on spectral features. A tentative assignment of the SERS bands was attempted for thermophilic bacteria. The results indicate that SERS can be a useful tool to study bacterial cell wall molecular differences.

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

scholarly journals ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

2021 ◽  
Author(s):  
Adarsh Kalikadien ◽  
Evgeny A. Pidko ◽  
Vivek Sinha
Keyword(s):  
Force Field ◽  
High Throughput ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Space Exploration ◽  
Molecular Structures ◽  
Data Driven ◽  
Pincer Complexes ◽  
Cobalt Porphyrin ◽  
Input Structure

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

scholarly journals ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees

2020 ◽  
Vol 36 (12) ◽  
pp. 3930-3931 ◽  
Author(s):  
Oliver B Scott ◽  
A W Edith Chan
Keyword(s):  
Open Source ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Diversity Analysis ◽  
Graph Analysis ◽  
Command Line ◽  
Molecular Scaffold ◽  
Large Sets ◽  
Command Line Tool ◽  
Scaffold Diversity

Abstract Summary ScaffoldGraph (SG) is an open-source Python library and command-line tool for the generation and analysis of molecular scaffold networks and trees, with the capability of processing large sets of input molecules. With the increase in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysis of chemical space, being used for visualization, clustering, scaffold-diversity analysis and active-series identification. Built on RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility and extendibility of the tool compared to existing software. Availability and implementation SG is freely available and released under the MIT licence at https://github.com/UCLCheminformatics/ScaffoldGraph.

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