Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

Bellmann, Louis; Klein, Raphael; Rarey, Matthias

doi:10.1021/acs.jcim.2c00334

Cited by 9 publications

(15 citation statements)

References 37 publications

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“…As shown in Table 3 , the papaverine and its metabolites met the necessities of drug likeness and passed their filters, such as the Veber filter (rotatable bonds ≤ 10 with TPSA ≤ 140) [ 27 , 28 ]. Furthermore, the papaverine and its metabolites were also verified with Lipinski’s rule of five (MW ≤ 500, HBA ≤ 10, HBD ≤ 5 and Log Po/w ≤ 5) [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Papaverinol-N-Oxide: A Microbial Biotransformation Product of Papaverine with Potential Antidiabetic and Antiobesity Activity Unveiled with In Silico Screening

et al. 2023

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Bioconversion of biosynthetic heterocyclic compounds has been utilized to produce new semisynthetic pharmaceuticals and study the metabolites of bioactive drugs used systemically. In this investigation, the biotransformation of natural heterocyclic alkaloid papaverine via filamentous fungi was explored. Molecular docking simulations, using protein tyrosine phosphatase 1B (PTP1B), α-glucosidase and pancreatic lipase (PL) as target enzymes, were performed to investigate the antidiabetic potential of papaverine and its metabolites in silico. The metabolites were isolated from biotransformation of papaverine with Cunninghamella elegans NRRL 2310, Rhodotorula rubra NRRL y1592, Penicillium chrysogeneum ATCC 10002 and Cunninghamella blackesleeana NRRL 1369 via reduction, demethylation, N-oxidation, oxidation and hydroxylation reactions. Seven metabolites were isolated: namely, 3,4-dihydropapaverine (metabolite 1), papaveroline (metabolite 2), 7-demethyl papaverine (metabolite 3), 6,4′-didemethyl papaverine (metabolite 4), papaverine-3-ol (metabolite 5), papaverinol (metabolite 6) and papaverinol N-oxide (metabolite 7). The structural elucidation of the metabolites was investigated with 1D and 2D NMR and mass spectroscopy (EI and ESI). The molecular docking studies showed that metabolite 7 exhibited better binding interactions with the target enzymes PTP1B, α-glucosidase and PL than did papaverine. Furthermore, papaverinol-N-oxide (7) also displayed inhibition of α-glucosidase and lipase enzymes comparable to that of their ligands (acarbose and orlistat, respectively), as unveiled with an in silico ADMET profile, molecular docking and molecular dynamics studies. In conclusion, this study provides evidence for enhanced inhibition of PTP1B, α-glucosidase and PL via some papaverine fungal transformation products and, therefore, potentially better antidiabetic and antiobesity effects than those of papaverine and other known therapeutic agents.

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Section: Resultsmentioning

confidence: 99%

Papaverinol-N-Oxide: A Microbial Biotransformation Product of Papaverine with Potential Antidiabetic and Antiobesity Activity Unveiled with In Silico Screening

et al. 2023

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“…For example, average lipophilicity, in vitro stability, and molecular obesity. Average lipophilicity and in vitro stability can be improved with drug delivery systems (antibody-drug conjugate, nanoencapsulation) [202,203], and synthetic procedures retrieved from combinatorial chemistry approaches [204][205][206]. However, to showcase an illustration of improved physicochemical properties, a joint venture of Arvinas and Pfizer developed a series of CRBN-based PROTACs (ARV-110 and ARV-471) that showed high clinical effectiveness, as shown in Figure 22.…”

Section: Discussionmentioning

confidence: 99%

Estrogen Receptor-α Targeting: PROTACs, SNIPERs, Peptide-PROTACs, Antibody Conjugated PROTACs and SNIPERs

2022

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Targeting selective estrogen subtype receptors through typical medicinal chemistry approaches is based on occupancy-driven pharmacology. In occupancy-driven pharmacology, molecules are developed in order to inhibit the protein of interest (POI), and their popularity is based on their virtue of faster kinetics. However, such approaches have intrinsic flaws, such as pico-to-nanomolar range binding affinity and continuous dosage after a time interval for sustained inhibition of POI. These shortcomings were addressed by event-driven pharmacology-based approaches, which degrade the POI rather than inhibit it. One such example is PROTACs (Proteolysis targeting chimeras), which has become one of the highly successful strategies of event-driven pharmacology (pharmacology that does the degradation of POI and diminishes its functions). The selective targeting of estrogen receptor subtypes is always challenging for chemical biologists and medicinal chemists. Specifically, estrogen receptor α (ER-α) is expressed in nearly 70% of breast cancer and commonly overexpressed in ovarian, prostate, colon, and endometrial cancer. Therefore, conventional hormonal therapies are most prescribed to patients with ER + cancers. However, on prolonged use, resistance commonly developed against these therapies, which led to selective estrogen receptor degrader (SERD) becoming the first-line drug for metastatic ER + breast cancer. The SERD success shows that removing cellular ER-α is a promising approach to overcoming endocrine resistance. Depending on the mechanism of degradation of ER-α, various types of strategies of developed.

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“…Currently, Enamine REAL Space contains over 38 billion compounds, including around 5.5 billion potential orally bioavailable drug‐like compounds that can be synthesized in few weeks at high success rate (>80%) and limited cost (usually in the order of 100–150 USD for 5 mg of compound) 10,11 . While Enamine REAL remains to be one of the most widely used sources for ultra‐large‐scale VS, there are numerous other libraries provided by different vendors and pharmaceutical companies including GalaXi (2.3 billion), 12 SAVI database (1.75B), 13 CHEMRiya (12 billion), 12 and the largest library up to date, eXplore, containing 2.7T molecules 14 . In parallel, many (if not all) pharmaceutical companies have created their own proprietary databases, such as Eli Lilly's Proximal Lilly Collection, 15 Pfizer's Global Virtual Library, 16 and GlaxoSmithKline's XXL collections 17 .…”

Section: Introductionmentioning

confidence: 99%

Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening

Kuan,

Radaeva,

Avenido

et al. 2023

WIREs Comput Mol Sci

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Recent efforts to synthetically expand drug‐like chemical libraries have led to the emergence of unprecedently large virtual databases. This surge of make‐on‐demand molecular datasets has been received enthusiastically across the drug discovery community as a new paradigm. In several recent studies, virtual screening (VS) of larger make‐on‐demand collections resulted in the identification of novel molecules with higher potency and specificity compared to more conventional VS campaigns relying on smaller in‐stock libraries. These results inspired ultra‐large VS against various clinically relevant targets, including key proteins of the SARS‐CoV‐2 virus. As library sizes rapidly surpassed the billion compounds mark, new computational screening strategies emerged, shifting from conventional docking to fragment‐based and machine learning‐accelerated methods. These approaches significantly reduce computational demands of ultra‐large screenings by lowering the number of molecules explicitly docked onto a target. Such strategies already demonstrated promise in evaluating libraries of tens of billions of molecules at relatively low computational cost. Herein, we review recent advancements in structure‐based methods for ultra‐large virtual screening that drug discovery practitioners have adopted to explore the ever‐expanding chemical universe.This article is categorized under: Data Science > Databases and Expert Systems Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Mechanics

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Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

Cited by 9 publications

References 37 publications

Papaverinol-N-Oxide: A Microbial Biotransformation Product of Papaverine with Potential Antidiabetic and Antiobesity Activity Unveiled with In Silico Screening

Papaverinol-N-Oxide: A Microbial Biotransformation Product of Papaverine with Potential Antidiabetic and Antiobesity Activity Unveiled with In Silico Screening

Estrogen Receptor-α Targeting: PROTACs, SNIPERs, Peptide-PROTACs, Antibody Conjugated PROTACs and SNIPERs

Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening

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