Flow chemistry as a tool to access novel chemical space for drug discovery

2020 ◽  
Vol 12 (17) ◽  
pp. 1547-1563
Author(s):  
Enol López ◽  
María Lourdes Linares ◽  
Jesús Alcázar
Keyword(s):  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Chemical Space ◽  
Flow Chemistry ◽  
Bioactive Molecules ◽  
Complex Molecules ◽  
Cycle Times

This perspective scrutinizes flow chemistry as a useful tool for medicinal chemists to expand the current chemical capabilities in drug discovery. This technology has demonstrated his value not only for the traditional reactions used in Pharma for the last 20 years, but also for bringing back to the lab underused chemistries to access novel chemical space. The combination with other technologies, such as photochemistry and electrochemistry, is opening new avenues for reactivity that will smoothen the access to complex molecules. The introduction of all these technologies in automated platforms will improve the productivity of medicinal chemistry labs reducing the cycle times to get novel and differentiated bioactive molecules, accelerating discovery cycle times.

scholarly journals The Generated Databases (GDBs) as a Source of 3D-shaped Building Blocks for Use in Medicinal Chemistry and Drug Discovery

2020 ◽  
Vol 74 (4) ◽  
pp. 241-246 ◽  
Author(s):  
Kris Meier ◽  
Sven Bühlmann ◽  
Josep Arús-Pous ◽  
Jean-Louis Reymond
Keyword(s):  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Organic Molecules ◽  
Chemical Space ◽  
Building Blocks ◽  
Chiral Molecules ◽  
Hydrogen Atoms ◽  
Quaternary Centers ◽  
Medical Needs ◽  
Unmet Medical Needs

Drug discovery is in constant need of new molecules to develop drugs addressing unmet medical needs. To assess the chemical space available for drug design, our group investigates the generated databases (GDBs) listing all possible organic molecules up to a defined size, the largest of which is GDB-17 featuring 166.4 billion molecules up to 17 non-hydrogen atoms. While known drugs and bioactive compounds are mostly aromatic and planar, the GDBs contain a plethora of non-aromatic 3D-shaped molecules, which are very useful for drug discovery since they generally have more desirable absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Here we review GDB enumeration methods and the selection and synthesis of GDB molecules as modulators of ion channels. We summarize the constitution of GDB subsets focusing on fragments (FDB17), medicinal chemistry (GDBMedChem) and ChEMBL-like molecules (GDBChEMBL), and the ring system database GDB4c as a rich source of novel 3D-shaped chiral molecules containing quaternary centers, such as the recently reported trinorbornane.

scholarly journals Continuous Flow Synthesis of Heterocycles: A Recent Update on the Flow Synthesis of Indoles

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3242 ◽  
Author(s):  
Marco Colella ◽  
Leonardo Degennaro ◽  
Renzo Luisi
Keyword(s):  
Drug Discovery ◽  
Continuous Flow ◽  
Medicinal Chemistry ◽  
Flow Chemistry ◽  
Modern Synthesis ◽  
Indole Derivatives ◽  
Flow Synthesis

Indole derivatives are among the most useful and interesting heterocycles employed in drug discovery and medicinal chemistry. In addition, flow chemistry and flow technology are changing the synthetic paradigm in the field of modern synthesis. In this review, the role of flow technology in the preparation of indole derivatives is showcased. Selected examples have been described with the aim to provide readers with an overview on the tactics and technologies used for targeting indole scaffolds.

scholarly journals Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery

2020 ◽  
Vol 11 (5) ◽  
pp. 1216-1225 ◽  
Author(s):  
Christine N. Morrison ◽  
Kathleen E. Prosser ◽  
Ryjul W. Stokes ◽  
Anna Cordes ◽  
Nils Metzler-Nolte ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Bioactive Molecules ◽  
3D Scaffolds ◽  
3 Dimensional ◽  
3D Space ◽  
New Strategy ◽  
Fragment Based Drug Discovery

Fragment-based drug discovery (FBDD) using 3-dimensional metallofragments is a new strategy for the identification of bioactive molecules.

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>

Metal-, and Organocatalyst-Free One-Pot Assembly of Chiral Aza-Tricyclic Molecules: Creating Six Contiguous Stereocenters from 2-D-Structures and an Amino Acid

2020 ◽  
Author(s):  
Dung Do
Keyword(s):  
Amino Acid ◽  
Chemical Space ◽  
Structural Diversity ◽  
Therapeutic Agents ◽  
Chiral Molecules ◽  
One Pot ◽  
Complex Molecules ◽  
Chiral Catalyst ◽  
Chiral Complex ◽  
Free Process

<p>Chiral molecules with their defined 3-D structures are of paramount importance for the study of chemical biology and drug discovery. Having rich structural diversity and unique stereoisomerism, chiral molecules offer a large chemical space that can be explored for the design of new therapeutic agents.<sup>1</sup> Practically, chiral architectures are usually prepared from organometallic and organocatalytic processes where a transition metal or an organocatalyst is tailor-made for desired reactions. As a result, developing a method that enables rapid assembly of chiral complex molecules under metal- and organocatalyst-free condition represents a daunting challenge. Here we developed a straightforward route to create a chiral 3-D structure from 2-D structures and an amino acid without any chiral catalyst. The center of this research is the design of a <a>special chiral spiroimidazolidinone cyclohexadienone intermediate</a>, a merger of a chiral reactive substrate with multiple nucleophillic/electrophillic sites and a transient organocatalyst. <a>This unique substrate-catalyst (“subcatalyst”) dual role of the intermediate enhances </a><a>the coordinational proximity of the chiral substrate and catalyst</a> in the key Aza-Michael/Michael cascade resulting in a substantial steric discrimination and an excellent overall diastereoselectivity. Whereas the “subcatalyst” (hidden catalyst) is not present in the reaction’s initial components, which renders a chiral catalyst-free process, it is strategically produced to promote sequential self-catalyzed reactions. The success of this methodology will pave the way for many efficient preparations of chiral complex molecules and aid for the quest to create next generation of therapeutic agents.</p>

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.

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

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