Charting, Navigating, and Populating Natural Product Chemical Space for Drug Discovery

2012 ◽  
Vol 55 (13) ◽  
pp. 5989-6001 ◽  
Author(s):  
Hugo Lachance ◽  
Stefan Wetzel ◽  
Kamal Kumar ◽  
Herbert Waldmann
Keyword(s):  
Drug Discovery ◽  
Natural Product ◽  
Chemical Space

scholarly journals Bridging the gap between natural product synthesis and drug discovery

2020 ◽  
Vol 37 (11) ◽  
pp. 1436-1453 ◽  
Author(s):  
Nathanyal J. Truax ◽  
Daniel Romo
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Chemical Space ◽  
Natural Product Synthesis ◽  
Chemical Information

Various synthetic strategies have been developed to explore natural products as an enduring source of chemical information useful for probing biological relevant chemical space and impacting drug discovery.

scholarly journals Cheminformatics to Characterize Pharmacologically Active Natural Products

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1566 ◽  
Author(s):  
José L. Medina-Franco ◽  
Fernanda I. Saldívar-González
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Chemical Space ◽  
Data Sets ◽  
Fragment Analysis ◽  
Chemical Structures ◽  
Speed Up ◽  
Pharmacologically Active ◽  
Design Experiments

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

scholarly journals From Target-Oriented to Motif-Oriented: A Case Study on Nannocystin Total Synthesis

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5327
Author(s):  
Weicheng Zhang
Keyword(s):  
Drug Discovery ◽  
Total Synthesis ◽  
Natural Product ◽  
Chemical Space ◽  
Organic Transformations ◽  
Total Syntheses ◽  
Target Molecule ◽  
Structure Activity ◽  
Structural Diversification

Natural product total synthesis is in essence target-oriented in that a set of organic transformations are orchestrated into a workable process, leading ultimately to the target molecule with a predefined architecture. For a bioactive lead, proof of synthetic viability is merely the beginning. Ensuing effort repurposes the initial synthesis for structural diversification in order to probe structure-activity relationship (SAR). Yet accessibility is not equal to flexibility; moving from convergency to divergency, it is not always feasible to explore the chemical space around a particular substructure of interest simply by tweaking an established route. In this situation, the motif-oriented strategy becomes a superior choice, which gives priority to synthetic flexibility at the concerned site such that a route is adopted only if it is capable of implementing diversification therein. This strategy was recently devised by Fürstner et al., enabling them to achieve total synthesis of both natural and non-natural nannocystins varied at an otherwise challenging position. The present review examines seven distinctive nannocystin total syntheses reported thus far and showcases the merits of conventional (target-oriented) as well as motif-oriented strategies, concluding that these two approaches complement each other and are both indispensable for natural product based drug discovery.

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.

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