ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Tingle, Benjamin I; Tang, Khanh; Castanon, Mar; Gutierrez, John J; Khurelbaatar, Munkhzul; Dandarchuluun, Chinzorig; Moroz, Yurii S.; Irwin, John J.

doi:10.1021/acs.jcim.2c01253

Cited by 43 publications

(36 citation statements)

References 33 publications

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“…With the determination of the cryo-EM structure of the SARS-CoV-2 Nsp10-Nsp14 complex (PDB ID 7N0B), and the development of a larger “tangible” ZINC22 library of 1.1 billion molecules, we launched a second docking screen. The 680 million molecules previously docked were a subset of this 1.1 billion molecule set; the key differences in docking came from the change in orientation of the side chain of Asp352 in the cryo-EM structure of SARS-CoV-2 Nsp14 (PDB ID 7N0B).…”

Section: Resultsmentioning

confidence: 99%

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

Singh

Fink

et al. 2023

J. Med. Chem.

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An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5′-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme’s SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

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Section: Resultsmentioning

confidence: 99%

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

Singh

Fink

et al. 2023

J. Med. Chem.

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“…The similarity baseline simply prioritizes the closest analogues of known actives and is related to standard approaches known as "hit expansion" or "analogue by catalogue." 4 To further explore why the ensemble methods did not improve the performance over the best individual models (Figures S1 and S2), we examined the overlap in actives retrieved between the ensembles and top models (Tables S2− S6). Although the ensembles found some actives not prioritized by the best RF-C model, the RF-C found many more actives, leading to better performance.…”

Section: Resultsmentioning

confidence: 99%

“…Commercial libraries, which are the primary source of compounds in academic screening efforts, can be grouped into two categories: “in-stock” and “synthesize-on-demand.” As of 2021, in-stock libraries comprise over 12 million molecules previously synthesized and physically stored by vendors . Synthesize-on-demand libraries are databases containing virtual molecules that a vendor considers to be readily accessible given stocks of available building blocks and established synthetic routes. , Such libraries now measure in the billions: the ZINC-22 database aggregates 37 billion compounds from the Enamine REAL, WuXi GalaXi, and Mcule Ultimate catalogues . Synthesize-on-demand libraries have superior chemical scaffold diversity and coverage of chemical shape characteristics, unlocking new possibilities to manipulate biological targets.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Alnammi,

Liu,

Ericksen

et al. 2023

J. Chem. Inf. Model.

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Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein–protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF’s 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

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“…We wondered if there might be many molecules in a general library that might be suitable to form symmetrical stacks in a fibril. To test how symmetry docking would work in a large library screen, we docked 22 million molecules from the ZINC22 database 36 against the GTP-1-bound form of AD PHF tau. We selected molecules that were similar in gross properties to GTP-1: neutral, with heavy atom count between 21 and 23, and clogP values between 3.30 and 3.50.…”

Section: Resultsmentioning

confidence: 99%

Docking for molecules that bind in a symmetric stack with SymDOCK

Smith,

Knight,

Kormos

et al. 2023

Preprint

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Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein’s symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand’s docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in aprospectivestudy SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

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ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Cited by 43 publications

References 33 publications

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Docking for molecules that bind in a symmetric stack with SymDOCK

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