scholarly journals Experimental validation of in silico target predictions on synergistic protein targets

MedChemComm ◽  
2013 ◽  
Vol 4 (1) ◽  
pp. 278-288 ◽  
Author(s):  
Isidro Cortes-Ciriano ◽  
Alexios Koutsoukas ◽  
Olga Abian ◽  
Robert C. Glen ◽  
Adrian Velazquez-Campoy ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Experimental Validation ◽  
Early Stage ◽  
Phenotypic Screening ◽  
Multiple Targets ◽  
Protein Targets ◽  
Screening Strategies ◽  
Recent Trends

Two relatively recent trends have become apparent in current early stage drug discovery settings: firstly, a revival of phenotypic screening strategies and secondly, the increasing acceptance that some drugs work by modulating multiple targets in parallel (‘multi-target drugs’).

Deployment of in silico and in vitro safety assays in early-stage drug discovery

2012 ◽  
Vol 4 (10) ◽  
pp. 1211-1213 ◽  
Author(s):  
Yvonne Will ◽  
Thomas Schroeter
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Early Stage

scholarly journals RECENT TRENDS IN IN-SILICO DRUG DISCOVERY

2015 ◽  
Vol 5 (1) ◽  
pp. 54 ◽  
Author(s):  
Sneha Rai ◽  
UTKARSH RAJ ◽  
Swapnil Tichkule ◽  
Himansu Kumar ◽  
Sonali Mishra ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Recent Trends

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

ADMET Profiling in Drug Discovery and Development: Perspectives of in silico, in vitro and integrated approaches

2021 ◽  
Vol 22 ◽  
Author(s):  
Nour El-Huda Daoud ◽  
Pobitra Borah ◽  
Pran Kishore Deb ◽  
Katharigatta N. Venugopala ◽  
Wafa Hourani ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Predictive Power ◽  
Early Stage ◽  
In Vitro Models ◽  
Prediction Ability ◽  
Drug Discovery And Development ◽  
In Silico Admet

: In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The early-stage ADMET profiling has introduced a new dimension to leading development. Although several high-throughput in vitro models are available for ADMET profiling, however, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate and, therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.

scholarly journals In Silico Strategies in Tuberculosis Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 665
Author(s):  
Stephani Joy Y. Macalino ◽  
Junie B. Billones ◽  
Voltaire G. Organo ◽  
Maria Constancia O. Carrillo
Keyword(s):  
Drug Resistance ◽  
Drug Discovery ◽  
In Silico ◽  
Global Public Health ◽  
Computational Tools ◽  
Protein Targets ◽  
Tb Treatment ◽  
Discovery Research ◽  
Poor Patient Compliance ◽  
Drug Discovery Research

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

scholarly journals Using the Structural Kinome to Systematize Kinase Drug Discovery

2021 ◽  
Author(s):  
Zheng Zhao ◽  
Philip E. Bourne
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Data Science ◽  
Early Stage ◽  
Structural Basis ◽  
Biomedical Data ◽  
Drug Molecules ◽  
Learning Techniques ◽  
Multiple Cell ◽  
Human Kinome

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases, when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure–activity relationships and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods – notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.

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