Artificial Intelligence and Cheminformatics-guided Modern Privileged Scaffold Research

2021 ◽  
Vol 21 ◽  
Author(s):  
Han-Yue Qiu ◽  
Rasmus Prætorius Clausen ◽  
Yun He ◽  
Hai-Liang Zhu
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
De Novo ◽  
Rapid Development ◽  
Bioactive Molecules ◽  
Complex Data ◽  
Related Research ◽  
Privileged Scaffold ◽  
Functional Shift ◽  
Privileged Scaffolds

Abstract: With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in the form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly important role in the drug discovery process. This development has also facilitated privileged scaffold-related research. By definition, a privileged scaffold is a structure that frequently occurs in diverse bioactive molecules. It either has a diverse family affinity or is selective to multiple family members in a superfamily, whilst is different from the “frequent hitters” or the “pan-assay interference compounds”. The long history of the use of this concept has witnessed a functional shift from stand-alone technology towards an integrated component in the drug discovery toolbox. Meanwhile, continuing efforts have been dedicated to deepen the understandings of the features of known privileged scaffolds. In this contribution, we focus on the current privileged scaffold-related research driven by state-of-art artificial intelligence approaches and cheminformatics. Representative cases with an emphasis on distinct research aspects are presented, including an update of the knowledge on privileged scaffolds; proof-of-concept tools and workflows to identify privileged scaffolds and to carry on de novo design; informatic SAR models with diversely complex data sets to provide an instructive prediction on new potential molecules bearing privileged scaffolds.

scholarly journals High-Throughput Quantitative Assay Technologies for Accelerating the Discovery and Optimization of Targeted Protein Degradation Therapeutics

2021 ◽  
pp. 247255522098504
Author(s):  
Jeffrey R. Simard ◽  
Linda Lee ◽  
Ellen Vieux ◽  
Reina Improgo ◽  
Trang Tieu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Protein Interactions ◽  
Fluorescent Protein ◽  
De Novo ◽  
Rapid Development ◽  
Dependent Manner ◽  
Western Blots ◽  
Advantages And Disadvantages ◽  
Degradation Rates

The aberrant regulation of protein expression and function can drastically alter cellular physiology and lead to numerous pathophysiological conditions such as cancer, inflammatory diseases, and neurodegeneration. The steady-state expression levels of endogenous proteins are controlled by a balance of de novo synthesis rates and degradation rates. Moreover, the levels of activated proteins in signaling cascades can be further modulated by a variety of posttranslational modifications and protein–protein interactions. The field of targeted protein degradation is an emerging area for drug discovery in which small molecules are used to recruit E3 ubiquitin ligases to catalyze the ubiquitination and subsequent degradation of disease-causing target proteins by the proteasome in both a dose- and time-dependent manner. Traditional approaches for quantifying protein level changes in cells, such as Western blots, are typically low throughput with limited quantification, making it hard to drive the rapid development of therapeutics that induce selective, rapid, and sustained protein degradation. In the last decade, a number of techniques and technologies have emerged that have helped to accelerate targeted protein degradation drug discovery efforts, including the use of fluorescent protein fusions and reporter tags, flow cytometry, time-resolved fluorescence energy transfer (TR-FRET), and split luciferase systems. Here we discuss the advantages and disadvantages associated with these technologies and their application to the development and optimization of degraders as therapeutics.

scholarly journals Artificial Intelligence in Drug Design

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2520 ◽  
Author(s):  
Gerhard Hessler ◽  
Karl-Heinz Baringhaus
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Drug Discovery ◽  
De Novo ◽  
Biologically Active ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Near Future

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

Intelligently Applying Artificial Intelligence in Chemoinformatics

2018 ◽  
Vol 18 (20) ◽  
pp. 1804-1826 ◽  
Author(s):  
Sahil Sharma ◽  
Deepak Sharma
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Graph Mining ◽  
De Novo ◽  
Molecular Design ◽  
Molecular Graph ◽  
Quantitative Structure Activity Relationship ◽  
Chemical Information ◽  
Design Synthesis

The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.

scholarly journals COV-DrugX Pipeline for Covid-19 Drug Repurposing

2021 ◽  
Author(s):  
Robin Sinha ◽  
Preeti P
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
The Novel ◽  
Novel Coronavirus ◽  
The Cost

The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. Despite the introduction of population-wide vaccination drives, countries such as Austria and Germany are witnessing the re-emergence of infections and deaths. Scientists, administrators and clinicians are scrambling to find solutions that include vaccines, and active therapeutic agents. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. Artificial intelligence (AI) enabled drug repurposing, has the potential to shorten the time and reduce the cost compared to de novo drug discovery.

scholarly journals Individual & Collective Human Intelligence in Drug Design: Evaluating the Search Strategy

2020 ◽  
Author(s):  
Giovanni Cincilla ◽  
Simone Masoni ◽  
Jascha Blobel
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
De Novo Drug Design ◽  
Research Fields

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find desired molecules (e.g. drug candidates). The objectives of this case study are: assess human intelligence in chemical space exploration problems; compare the performance of individual and collective human intelligence; and contrast human and artificial intelligence achievements in de novo drug design. To our knowledge this is the first time that human intelligence is being evaluated for such a task in drug discovery and, of similar importance, compared to the performance of artificial intelligence (e.g. machine learning, genetic algorithms), giving first insights towards their differences and uniqueness.

scholarly journals Individual & Collective Human Intelligence in Drug Design: Evaluating the Search Strategy

2020 ◽  
Author(s):  
Giovanni Cincilla ◽  
Simone Masoni ◽  
Jascha Blobel
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
De Novo Drug Design ◽  
Research Fields

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find desired molecules (e.g. drug candidates). The objectives of this case study are: assess human intelligence in chemical space exploration problems; compare the performance of individual and collective human intelligence; and contrast human and artificial intelligence achievements in de novo drug design. To our knowledge this is the first time that human intelligence is being evaluated for such a task in drug discovery and, of similar importance, compared to the performance of artificial intelligence (e.g. machine learning, genetic algorithms), giving first insights towards their differences and uniqueness.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

The applications of next-generation sequencing (NGS) in drug development for cancer therapy

2020 ◽  
Vol 16 ◽  
Author(s):  
Pelin Telkoparan-Akillilar ◽  
Dilek Cevik
Keyword(s):  
Next Generation Sequencing ◽  
Drug Discovery ◽  
Cancer Therapy ◽  
Target Identification ◽  
Rapid Development ◽  
Next Generation ◽  
Biomedical Sciences ◽  
Dna And Rna ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Background: Numerous sequencing techniques have been progressed since the 1960s with the rapid development of molecular biology studies focusing on DNA and RNA. Methods: a great number of articles, book chapters, websites are reviewed, and the studies covering NGS history, technology and applications to cancer therapy are included in the present article. Results: High throughput next-generation sequencing (NGS) technologies offer many advantages over classical Sanger sequencing with decreasing cost per base and increasing sequencing efficiency. NGS technologies are combined with bioinformatics software to sequence genomes to be used in diagnostics, transcriptomics, epidemiologic and clinical trials in biomedical sciences. The NGS technology has also been successfully used in drug discovery for the treatment of different cancer types. Conclusion: This review focuses on current and potential applications of NGS in various stages of drug discovery process, from target identification through to personalized medicine.

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