scholarly journals Encoded Library Technologies as Integrated Lead Finding Platforms for Drug Discovery

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1629 ◽  
Author(s):  
Johannes Ottl ◽  
Lukas Leder ◽  
Jonas V. Schaefer ◽  
Christoph E. Dumelin
Keyword(s):  
Drug Discovery ◽  
Cyclic Peptides ◽  
Chemical Space ◽  
Pharmaceutical Research ◽  
Peptide Libraries ◽  
Assay Development ◽  
Low Molecular Weight ◽  
Target Validation ◽  
Lead Finding ◽  
Chemical Matter

The scope of targets investigated in pharmaceutical research is continuously moving into uncharted territory. Consequently, finding suitable chemical matter with current compound collections is proving increasingly difficult. Encoded library technologies enable the rapid exploration of large chemical space for the identification of ligands for such targets. These binders facilitate drug discovery projects both as tools for target validation, structural elucidation and assay development as well as starting points for medicinal chemistry. Novartis internalized two complementing encoded library platforms to accelerate the initiation of its drug discovery programs. For the identification of low-molecular weight ligands, we apply DNA-encoded libraries. In addition, encoded peptide libraries are employed to identify cyclic peptides. This review discusses how we apply these two platforms in our research and why we consider it beneficial to run both pipelines in-house.

scholarly journals High-Throughput Screening: Update on Practices and Success

2006 ◽  
Vol 11 (7) ◽  
pp. 864-869 ◽  
Author(s):  
Sandra Fox ◽  
Shauna Farr-Jones ◽  
Lynne Sopchak ◽  
Amy Boggs ◽  
Helen Wang Nicely ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
New Technologies ◽  
Assay Development ◽  
Target Validation ◽  
Fully Integrated ◽  
Drug Candidates ◽  
Secondary Screening ◽  
Biotechnology Companies

High-throughput screening (HTS) has become an important part of drug discovery at most pharmaceutical and many biotechnology companies worldwide, and use of HTS technologies is expanding into new areas. Target validation, assay development, secondary screening, ADME/Tox, and lead optimization are among the areas in which there is an increasing use of HTS technologies. It is becoming fully integrated within drug discovery, both upstream and downstream, which includes increasing use of cell-based assays and high-content screening (HCS) technologies to achieve more physiologically relevant results and to find higher quality leads. In addition, HTS laboratories are continually evaluating new technologies as they struggle to increase their success rate for finding drug candidates. The material in this article is based on a 900-page HTS industry report involving 54 HTS directors representing 58 HTS laboratories and 34 suppliers.

Natural Thiazoline-Based Cyclodepsipeptides from Marine Cyanobacteria: Chemistry, Bioefficiency and Clinical Aspects

2021 ◽  
Vol 28 ◽  
Author(s):  
Rajiv Dahiya ◽  
Sunita Dahiya ◽  
Neeraj Kumar Fuloria ◽  
Satish Jankie ◽  
Alka Agarwal ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Drug Targets ◽  
Cyclic Peptides ◽  
Chemical Space ◽  
Clinical Success ◽  
Clinical Status ◽  
Structural Features ◽  
Clinical Aspects ◽  
Medical Needs

Background: Peptides and peptide-based therapeutics are biomolecules that demarcate a significant chemical space to bridge small molecules with biological therapeutics such as antibodies, recombinant proteins, and protein domains. Introduction: Cyclooligopeptides and depsipeptides, particularly cyanobacteria-derived thiazoline-based cyclopolypeptides (CTBCs), exhibit a wide array of pharmacological activities due to their unique structural features and interesting bioactions, which furnish them as promising leads for drug discovery. Method: In the present study, we comprehensively review the natural sources, distinguishing chemistries, and pertinent bioprofiles of CTBCs. We analyze their structural peculiarities counting the mode of actions for biological portrayals which render CTBCs as indispensable sources for the emergence of prospective peptide-based therapeutics. In this milieu, metal organic frameworks and their biomedical applications are also briefly discussed. To boot, the challenges, approaches, and clinical status of peptide-based therapeutics are conferred. Result: Based on these analyses, CTBCs can be appraised as ideal drug targets that have always remained a challenge for traditional small molecules, like those involved in protein-protein interactions or to be developed as potential cancer-targeting nanomaterials. Cyclization-induced reduced conformational freedom of these cyclooligopeptides contribute to improved metabolic stability and binding affinity to their molecular targets. Clinical success of several cyclic peptides provokes the large library-screening and synthesis of natural product-like cyclic peptides to address the unmet medical needs. Conclusion: CTBCs can be considered as the most promising lead compounds for drug discovery. Adopting the amalgamation of advanced biological and biopharmaceutical strategies might endure these cyclopeptides to be prospective biomolecules for futuristic therapeutic applications in the coming times.

Total Synthesis of Axially-Chiral Cannabinols: A New Platform for Cannabinoid-Based Drug Discovery

2019 ◽  
Author(s):  
Primali Navaratne ◽  
Jenny Wilkerson ◽  
Kavindri Ranasinghe ◽  
Evgeniya Semenova ◽  
Lance McMahon ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Total Synthesis ◽  
Ground State ◽  
Chemical Space ◽  
Modern Medicine ◽  
The Novel ◽  
Pharmaceutical Development ◽  
Bioactive Component ◽  
Axially Chiral ◽  
New Directions

<div> <div> <div> <p>Phytocannabinoids, molecules isolated from cannabis, are gaining attention as promising leads in modern medicine, including pain management. Considering the urgent need for combating the opioid crisis, new directions for the design of cannabinoid-inspired analgesics are of immediate interest. In this regard, we have hypothesized that axially-chiral-cannabinols (ax-CBNs), unnatural (and unknown) isomers of cannabinol (CBN) may be valuable scaffolds for cannabinoid-inspired drug discovery. There are multiple reasons for thinking this: (a) ax-CBNs would have ground-state three-dimensionality akin to THC, a key bioactive component of cannabis, (b) ax-CBNs at their core structure are biaryl molecules, generally attractive platforms for pharmaceutical development due to their ease of functionalization and stability, and (c) atropisomerism with respect to phytocannabinoids is unexplored “chemical space.” Herein we report a scalable total synthesis of ax-CBNs, examine physical properties experimentally and computationally, and provide preliminary behavioral and analgesic analysis of the novel scaffolds. </p> </div> </div> </div>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Exploring and exploiting plant cyclic peptides for drug discovery and development

2021 ◽  
Author(s):  
Jingjing Zhang ◽  
Jimin Yuan ◽  
Zhijie Li ◽  
Chunjin Fu ◽  
Menglong Xu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Cyclic Peptides ◽  
Drug Discovery And Development

Screening the receptorome: an efficient approach for drug discovery and target validation

2006 ◽  
Vol 11 (15-16) ◽  
pp. 708-716 ◽  
Author(s):  
Ryan T. Strachan ◽  
Gina Ferrara ◽  
Bryan L. Roth
Keyword(s):  
Drug Discovery ◽  
Target Validation ◽  
Efficient Approach

Visualization of the Chemical Space in Drug Discovery

2008 ◽  
Vol 4 (4) ◽  
pp. 322-333 ◽  
Author(s):  
Jose Medina-Franco ◽  
Karina Martinez-Mayorga ◽  
Marc Giulianotti ◽  
Richard Houghten ◽  
Clemencia Pinilla
Keyword(s):  
Drug Discovery ◽  
Chemical Space
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