scholarly journals Structure‐Based Drug Design (SBDD) and In Silico Pharmacophore Screening Enabled the Discovery of Small Organic Molecules and Peptides Modulators of Bla C, TEM‐1 and Amp C Beta Lactamases

2021 ◽  
Vol 35 (S1) ◽  
Author(s):  
Cristina Clement ◽  
Janet Gonzalez ◽  
Sheuli Zakia ◽  
Manfred Philipp
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Organic Molecules ◽  
Structure Based Drug Design ◽  
Small Organic Molecules ◽  
Beta Lactamases ◽  
Pharmacophore Screening

scholarly journals Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

2018 ◽  
Author(s):  
Traci Clymer ◽  
Vanessa Vargas ◽  
Eric Corcoran ◽  
Robin Kleinberg ◽  
Jakub Kostal
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Adverse Effects ◽  
Environmental Health ◽  
In Silico ◽  
Adverse Outcomes ◽  
Design Stage ◽  
Hazardous Chemicals ◽  
Structure Based Drug Design ◽  
Computational Drug Discovery

Chemicals are the basis of our society and economy, yet many existing chemicals are known to have unintended adverse effects on human and environmental health. Testing all existing and new chemicals on animals is both economically and ethically unfeasible. In this paper, a new in silico framework is presented that affords redesign of existing hazardous chemicals in commerce based on specific molecular initiating events in their adverse outcomes pathways. Our approach is based on a successful methodology implemented in computational drug discovery, and promises to dramatically lower costs associated with new chemical development by synergistically addressing chemical function and safety at the design stage. <br>

NERDD: a web portal providing access to in silico tools for drug discovery

2019 ◽  
Author(s):  
Conrad Stork ◽  
Gerd Embruch ◽  
Martin Šícho ◽  
Christina de Bruyn Kops ◽  
Ya Chen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Organic Molecules ◽  
Web Portal ◽  
Small Organic Molecules ◽  
Biological Assays ◽  
Software Packages ◽  
Large Compound ◽  
Local Installation ◽  
In Silico Tools

Abstract Summary The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development. Availability and implementation The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation. Contact [email protected]

scholarly journals Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

2018 ◽  
Author(s):  
Traci Clymer ◽  
Vanessa Vargas ◽  
Eric Corcoran ◽  
Robin Kleinberg ◽  
Jakub Kostal
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Adverse Effects ◽  
Environmental Health ◽  
In Silico ◽  
Adverse Outcomes ◽  
Design Stage ◽  
Hazardous Chemicals ◽  
Structure Based Drug Design ◽  
Computational Drug Discovery

Chemicals are the basis of our society and economy, yet many existing chemicals are known to have unintended adverse effects on human and environmental health. Testing all existing and new chemicals on animals is both economically and ethically unfeasible. In this paper, a new in silico framework is presented that affords redesign of existing hazardous chemicals in commerce based on specific molecular initiating events in their adverse outcomes pathways. Our approach is based on a successful methodology implemented in computational drug discovery, and promises to dramatically lower costs associated with new chemical development by synergistically addressing chemical function and safety at the design stage. <br>

scholarly journals Target2DeNovoDrug : a novel programmatic tool for deep learning based de novo drug design for a target of interest

2020 ◽  
Author(s):  
Rafal Madaj ◽  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
In Silico ◽  
High Performance ◽  
Data Science ◽  
De Novo ◽  
Computationally Efficient ◽  
De Novo Drug Design ◽  
Necrosis Factor Alpha ◽  
Structure Based Drug Design

The on-going data-science and AI revolution offers researchers with fresh set of tools to approach structure-based drug design problems in the computer aided drug design space. A novel programmatic tool that can be used in aid of in silico-deep learning based de novo drug design for any target of interest has been reported. Once the user specifies the target of interest, the programmatic workflow of the tool generates novel SMILES of compounds that are likely to be active against the target. The tool also performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results in the working folder of the user. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained and supported at the GitHub repository given in link below https://github.com/bengeof/Target2DeNovoDrug

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