A return to rational drug discovery: computer-based models of cells, organs and systems in drug target identification

2000 ◽  
Vol 4 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Denis Noble ◽  
Thomas J Colatsky
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Target Identification ◽  
Rational Drug ◽  
Drug Target Identification ◽  
Computer Based

scholarly journals Rationalizing Viral Drug Target Identification Using Computational Approaches : The SARS-CoV-2 Spike Glycoprotein S Case Study

2020 ◽  
Author(s):  
Petr Popov ◽  
Pavel Buslaev ◽  
Igor Kozlovskii ◽  
Mark Zaretskii ◽  
Dmitry Karlov ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Binding Site ◽  
Binding Sites ◽  
Drug Target ◽  
Conformational Transition ◽  
Target Identification ◽  
Drug Binding ◽  
Spike Glycoprotein ◽  
Important Amino Acid ◽  
Drug Target Identification

<div><div><div><p>COVID-19 emphasized the need for fast reaction tools to fight global biological threats such as viruses. Rapid drug discovery is one of the strategies for efficient social response. The success of a drug discovery campaign critically depends on the selected drug target, and the wrong target nullifies all the efforts to develop a drug. Viral drug target identification is a challenging problem, and computational methods can reduce the number of candidate targets. Here we present a structure-based approach to identify vulnerable regions in viral proteins that comprise drug binding sites. To detect promising binding sites, we take into account protein dynamics, accessibility, and mutability of the binding site, coupled with the putative mechanism of action of a drug. Applying to the SARS-CoV-2 Spike Glycoprotein S, we observed conformation- and oligomer-specific glycan-free binding site that is proximal to the receptor binding domain and comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with drug-like molecules docked into the binding sites revealed shifted equilibrium towards the inactive conformation compared to the ligand-free simulations. Small molecules targeting this binding site could prevent the closed-to-open conformational transition of the Spike protein, thus, allosterically inhibit the interaction with the human angiotensin-converting enzyme 2 receptor.</p></div></div></div>

scholarly journals Rationalizing Viral Drug Target Identification Using Computational Approaches : The SARS-CoV-2 Spike Glycoprotein S Case Study

2020 ◽  
Author(s):  
Petr Popov ◽  
Pavel Buslaev ◽  
Igor Kozlovskii ◽  
Mark Zaretskii ◽  
Dmitry Karlov ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Binding Site ◽  
Binding Sites ◽  
Drug Target ◽  
Conformational Transition ◽  
Target Identification ◽  
Drug Binding ◽  
Spike Glycoprotein ◽  
Important Amino Acid ◽  
Drug Target Identification

<div><div><div><p>COVID-19 emphasized the need for fast reaction tools to fight global biological threats such as viruses. Rapid drug discovery is one of the strategies for efficient social response. The success of a drug discovery campaign critically depends on the selected drug target, and the wrong target nullifies all the efforts to develop a drug. Viral drug target identification is a challenging problem, and computational methods can reduce the number of candidate targets. Here we present a structure-based approach to identify vulnerable regions in viral proteins that comprise drug binding sites. To detect promising binding sites, we take into account protein dynamics, accessibility, and mutability of the binding site, coupled with the putative mechanism of action of a drug. Applying to the SARS-CoV-2 Spike Glycoprotein S, we observed conformation- and oligomer-specific glycan-free binding site that is proximal to the receptor binding domain and comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with drug-like molecules docked into the binding sites revealed shifted equilibrium towards the inactive conformation compared to the ligand-free simulations. Small molecules targeting this binding site could prevent the closed-to-open conformational transition of the Spike protein, thus, allosterically inhibit the interaction with the human angiotensin-converting enzyme 2 receptor.</p></div></div></div>

scholarly journals Small molecule photocatalysis enables drug target identification via energy transfer

2021 ◽  
Author(s):  
Aaron D Trowbridge ◽  
Ciaran P Seath ◽  
Frances P Rodriguez-Rivera ◽  
Beryl X Li ◽  
Barbara E Dul ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Drug Discovery ◽  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
High Energy ◽  
Therapeutic Benefit ◽  
Cellular Targets ◽  
Cellular Environment ◽  
Drug Target Identification

The identification of cellular targets that can be exploited for therapeutic benefit, broadly known as target ID, remains a fundamental goal in drug discovery. In recent years, the application of new chemical and biological technologies that accelerate target ID has become commonplace within drug discovery programs, as a complete understanding of how molecules react in a cellular environment can lead to increased binding selectivity, improved safety profiles, and clinical efficacy. Established approaches using photoaffinity labelling (PAL) are often costly and time-consuming due to poor signal-to-noise coupled with extensive probe optimization. Such challenges are exacerbated when dealing with low abundance membrane proteins or multiple protein target engagement, typically rendering target ID unfeasible. Herein, we describe a general platform for photocatalytic small molecule target ID, which hinges upon the generation of high-energy carbene intermediates via visible light-mediated Dexter energy transfer. By decoupling the reactive warhead from the drug, catalytic signal amplification results in multiple labelling events per drug, leading to unprecedented levels of target enrichment. Through the development of cell permeable photocatalyst conjugates, this method has enabled the quantitative target and off target identification of several drugs including (+)-JQ1, paclitaxel, and dasatinib. Moreover, this methodology has led to the target ID of two GPCRs, ADORA2A and GPR40m, a class of drug target seldom successfully uncovered in small molecule PAL campaigns.

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

scholarly journals Some Perspectives on Network Modeling in Therapeutic Target Prediction

2013 ◽  
Vol 5 ◽  
pp. BECB.S10793 ◽  
Author(s):  
Reka Albert ◽  
Bhaskar DasGupta ◽  
Nasim Mobasheri
Keyword(s):  
Therapeutic Target ◽  
Drug Target ◽  
Biological Network ◽  
Target Identification ◽  
Target Prediction ◽  
Identification Problem ◽  
Pharmaceutical Companies ◽  
Specific Behavior ◽  
Drug Target Identification ◽  
Key Steps

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

Drug Target Identification in Tissues by Thermal Proteome Profiling

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
André Mateus ◽  
Nils Kurzawa ◽  
Jessica Perrin ◽  
Giovanna Bergamini ◽  
Mikhail M. Savitski
Keyword(s):  
Drug Target ◽  
Target Identification ◽  
Annual Review ◽  
Publication Date ◽  
Proteome Profiling ◽  
Biological Phenomena ◽  
Target Deconvolution ◽  
Drug Target Identification ◽  
Recent Developments ◽  
Fundamental Biology

Drug target deconvolution can accelerate the drug discovery process by identifying a drug's targets (facilitating medicinal chemistry efforts) and off-targets (anticipating toxicity effects or adverse drug reactions). Multiple mass spectrometry–based approaches have been developed for this purpose, but thermal proteome profiling (TPP) remains to date the only one that does not require compound modification and can be used to identify intracellular targets in living cells. TPP is based on the principle that the thermal stability of a protein can be affected by its interactions. Recent developments of this approach have expanded its applications beyond drugs and cell cultures to studying protein-drug interactions and biological phenomena in tissues. These developments open up the possibility of studying drug treatment or mechanisms of disease in a holistic fashion, which can result in the design of better drugs and lead to a better understanding of fundamental biology. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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