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.

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 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’).

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 Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

2021 ◽  
Author(s):  
Ruby Srivastava
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Repurposing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Modern Drug ◽  
Short Period

Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.

scholarly journals QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

2021 ◽  
Author(s):  
Ben Geoffrey ◽  
Rafal Madaj ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Data Science ◽  
De Novo ◽  
Autodock Vina ◽  
De Novo Drug Design ◽  
Ligand Interaction

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for aparticular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activityagainst the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of theinteraction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock- Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in thefollowing GitHub repositoryhttps://github.com/bengeof/Target2DeNovoDrugPropMaxAnticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into our classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided belowhttps://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax

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