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

<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>

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

Molecules ◽

10.3390/molecules25225327 ◽

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.

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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

10.26434/chemrxiv.7841270.v2 ◽

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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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

10.26434/chemrxiv.7841270 ◽

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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General Cyclopropane Assembly via Enantioselective Redox-Active Carbene Transfer to Aliphatic Olefins

10.26434/chemrxiv.7436795 ◽

2018 ◽

Author(s):

Marc Montesinos-Magraner ◽

Matteo Costantini ◽

Rodrigo Ramirez-Contreras ◽

Michael E. Muratore ◽

Magnus J. Johansson ◽

...

Keyword(s):

Total Synthesis ◽

Asymmetric Synthesis ◽

Chemical Space ◽

Synthetic Approach ◽

Asymmetric Cyclopropanation ◽

Redox Active ◽

Wide Range ◽

Leaving Group ◽

Stereoelectronic Properties ◽

Carbene Transfer

Asymmetric cyclopropane synthesis currently requires bespoke strategies, methods, substrates and reagents, even when targeting similar compounds. This limits the speed and chemical space available for discovery campaigns. Here we introduce a practical and versatile diazocompound, and we demonstrate its performance in the first unified asymmetric synthesis of functionalized cyclopropanes. We found that the redox-active leaving group in this reagent enhances the reactivity and selectivity of geminal carbene transfer. This effect enabled the asymmetric cyclopropanation of a wide range of olefins including unactivated aliphatic alkenes, enabling the 3-step total synthesis of (–)-dictyopterene A. This unified synthetic approach delivers high enantioselectivities that are independent of the stereoelectronic properties of the functional groups transferred. Our results demonstrate that orthogonally-differentiated diazocompounds are viable and advantageous equivalents of single-carbon chirons<i>.</i>

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

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557520666200429102334 ◽

2020 ◽

Vol 20 (14) ◽

pp. 1375-1388 ◽

Cited By ~ 2

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.

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