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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.

Molecular docking is a pragmatic approach to exploit protein structures for new ligand discovery, but the growing size of available chemical space is increasingly challenging to screen on in-house computer clusters. We have therefore developed AWS-DOCK, a protocol for running UCSF DOCK in the AWS cloud. Our approach leverages the low cost and scalability of cloud resources combined with a low-molecule-cost docking engine to screen billions of molecules efficiently. We benchmarked our system by screening 50 million HAC 22 molecules against the DRD4 receptor with an average CPU time of around 1 s per molecule. We saw up to 3-fold variations in cost between AWS availability zones. Docking 4.5 billion lead-like molecules, a 7 week calculation on our 1000-core lab cluster, runs in about a week depending on accessible CPUs, in AWS for around $25,000, less than the cost of two new nodes. The cloud docking protocol is described in easy-to-follow steps and may be sufficiently general to be used for other docking programs. All the tools to enable AWS-DOCK are available free to everyone, while DOCK 3.8 is free for academic research.

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Identifying and purchasing new small molecules to test in biological assays are enabling for ligand discovery, but as purchasable chemical space continues to grow into the tens of billions based on inexpensive make-on-demand compounds, simply searching this space becomes a major challenge. We have therefore developed ZINC20, a new version of ZINC with two major new features: billions of new molecules and new methods to search them. As a fully enumerated database, ZINC can be searched precisely using explicit atomic-level graph-based methods, such as SmallWorld for similarity and Arthor for pattern and substructure search, as well as 3D methods such as docking. Analysis of the new make-on-demand compound sets by these and related tools reveals startling features. For instance, over 97% of the core Bemis-Murcko scaffolds in make-on-demand libraries are unavailable from "in-stock" collections. Correspondingly, the number of new Bemis-Murcko scaffolds is rising almost as a linear fraction of the elaborated molecules. Thus, an 88-fold increase in the number of molecules in the make-on-demand versus the in-stock sets is built upon a 16-fold increase in the number of Bemis-Murcko scaffolds. The make-on-demand library is also more structurally diverse than physical libraries, with a massive increase in disc- and sphere-like shaped molecules. The new system is freely available at zinc20.docking.org.

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Many questions about the biological activity and availability of small molecules remain inaccessible to investigators who could most benefit from their answers. To narrow the gap between chemoinformatics and biology, we have developed a suite of ligand annotation, purchasability, target, and biology association tools, incorporated into ZINC and meant for investigators who are not computer specialists. The new version contains over 120 million purchasable "drug-like" compounds--effectively all organic molecules that are for sale--a quarter of which are available for immediate delivery. ZINC connects purchasable compounds to high-value ones such as metabolites, drugs, natural products, and annotated compounds from the literature. Compounds may be accessed by the genes for which they are annotated as well as the major and minor target classes to which those genes belong. It offers new analysis tools that are easy for nonspecialists yet with few limitations for experts. ZINC retains its original 3D roots--all molecules are available in biologically relevant, ready-to-dock formats. ZINC is freely available at http://zinc15.docking.org.

Colloidal aggregation of organic molecules is the dominant mechanism for artifactual inhibition of proteins, and controls against it are widely deployed. Notwithstanding an increasingly detailed understanding of this phenomenon, a method to reliably predict aggregation has remained elusive. Correspondingly, active molecules that act via aggregation continue to be found in early discovery campaigns and remain common in the literature. Over the past decade, over 12 thousand aggregating organic molecules have been identified, potentially enabling a precedent-based approach to match known aggregators with new molecules that may be expected to aggregate and lead to artifacts. We investigate an approach that uses lipophilicity, affinity, and similarity to known aggregators to advise on the likelihood that a candidate compound is an aggregator. In prospective experimental testing, five of seven new molecules with Tanimoto coefficients (Tc's) between 0.95 and 0.99 to known aggregators aggregated at relevant concentrations. Ten of 19 with Tc's between 0.94 and 0.90 and three of seven with Tc's between 0.89 and 0.85 also aggregated. Another three of the predicted compounds aggregated at higher concentrations. This method finds that 61 827 or 5.1% of the ligands acting in the 0.1 to 10 μM range in the medicinal chemistry literature are at least 85% similar to a known aggregator with these physical properties and may aggregate at relevant concentrations. Intriguingly, only 0.73% of all drug-like commercially available compounds resemble the known aggregators, suggesting that colloidal aggregators are enriched in the literature. As a percentage of the literature, aggregator-like compounds have increased 9-fold since 1995, partly reflecting the advent of high-throughput and virtual screens against molecular targets. Emerging from this study is an aggregator advisor database and tool ( http://advisor.bkslab.org ), free to the community, that may help distinguish between fruitful and artifactual screening hits acting by this mechanism.

PurposePrevious studies showed that lowering PrP(C) concomitantly reduced PrP(Sc) in the brains of mice inoculated with prions. We aimed to develop assays that measure PrP(C) on the surface of human T98G glioblastoma and IMR32 neuroblastoma cells. Using these assays, we sought to identify chemical hits, confirmed hits, and scaffolds that potently lowered PrP(C) levels in human brains cells, without lethality, and that could achieve drug concentrations in the brain after oral or intraperitoneal dosing in mice.MethodsWe utilized HTS ELISA assays to identify small molecules that lower PrP(C) levels by ≥30% on the cell surface of human glioblastoma (T98G) and neuroblastoma (IMR32) cells.ResultsFrom 44,578 diverse chemical compounds tested, 138 hits were identified by single point confirmation (SPC) representing 7 chemical scaffolds in T98G cells, and 114 SPC hits representing 6 scaffolds found in IMR32 cells. When the confirmed SPC hits were combined with structurally related analogs, >300 compounds (representing 6 distinct chemical scaffolds) were tested for dose-response (EC₅₀) in both cell lines, only studies in T98G cells identified compounds that reduced PrP(C) without killing the cells. EC₅₀ values from 32 hits ranged from 65 nM to 4.1 μM. Twenty-eight were evaluated in vivo in pharmacokinetic studies after a single 10 mg/kg oral or intraperitoneal dose in mice. Our results showed brain concentrations as high as 16.2 μM, but only after intraperitoneal dosing.ConclusionsOur studies identified leads for future studies to determine which compounds might lower PrP(C) levels in rodent brain, and provide the basis of a therapeutic for fatal disorders caused by PrP prions.

PurposePrevious studies showed that lowering PrP(C) concomitantly reduced PrP(Sc) in the brains of mice inoculated with prions. We aimed to develop assays that measure PrP(C) on the surface of human T98G glioblastoma and IMR32 neuroblastoma cells. Using these assays, we sought to identify chemical hits, confirmed hits, and scaffolds that potently lowered PrP(C) levels in human brains cells, without lethality, and that could achieve drug concentrations in the brain after oral or intraperitoneal dosing in mice.MethodsWe utilized HTS ELISA assays to identify small molecules that lower PrP(C) levels by ≥30% on the cell surface of human glioblastoma (T98G) and neuroblastoma (IMR32) cells.ResultsFrom 44,578 diverse chemical compounds tested, 138 hits were identified by single point confirmation (SPC) representing 7 chemical scaffolds in T98G cells, and 114 SPC hits representing 6 scaffolds found in IMR32 cells. When the confirmed SPC hits were combined with structurally related analogs, >300 compounds (representing 6 distinct chemical scaffolds) were tested for dose-response (EC₅₀) in both cell lines, only studies in T98G cells identified compounds that reduced PrP(C) without killing the cells. EC₅₀ values from 32 hits ranged from 65 nM to 4.1 μM. Twenty-eight were evaluated in vivo in pharmacokinetic studies after a single 10 mg/kg oral or intraperitoneal dose in mice. Our results showed brain concentrations as high as 16.2 μM, but only after intraperitoneal dosing.ConclusionsOur studies identified leads for future studies to determine which compounds might lower PrP(C) levels in rodent brain, and provide the basis of a therapeutic for fatal disorders caused by PrP prions.

During prion diseases, a normally benign, host protein, denoted PrP(C), undergoes alternative folding into the aberrant isoform, PrP(Sc). We used ELISA to identify and confirm hits in order to develop leads that reduce PrP(Sc) in prion-infected dividing and stationary-phase mouse neuroblastoma (ScN2a-cl3) cells. We tested 52,830 diverse small molecules in dividing cells and 49,430 in stationary-phase cells. This led to 3100 HTS and 970 single point confirmed (SPC) hits in dividing cells, 331 HTS and 55 confirmed SPC hits in stationary-phase cells as well as 36 confirmed SPC hits active in both. Fourteen chemical leads were identified from confirmed SPC hits in dividing cells and three in stationary-phase cells. From more than 682 compounds tested in concentration-effect relationships in dividing cells to determine potency (EC50), 102 had EC50 values between 1 and 10 μM and 50 had EC50 values of

During prion diseases, a normally benign, host protein, denoted PrP(C), undergoes alternative folding into the aberrant isoform, PrP(Sc). We used ELISA to identify and confirm hits in order to develop leads that reduce PrP(Sc) in prion-infected dividing and stationary-phase mouse neuroblastoma (ScN2a-cl3) cells. We tested 52,830 diverse small molecules in dividing cells and 49,430 in stationary-phase cells. This led to 3100 HTS and 970 single point confirmed (SPC) hits in dividing cells, 331 HTS and 55 confirmed SPC hits in stationary-phase cells as well as 36 confirmed SPC hits active in both. Fourteen chemical leads were identified from confirmed SPC hits in dividing cells and three in stationary-phase cells. From more than 682 compounds tested in concentration-effect relationships in dividing cells to determine potency (EC50), 102 had EC50 values between 1 and 10 μM and 50 had EC50 values of

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A(2A) adenosine receptor, based on complementarity to its recently determined structure. The A(2A) adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 microM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A(2A) versus the related A(1) and A(3) subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A(2A) adenosine receptor, based on complementarity to its recently determined structure. The A(2A) adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 microM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A(2A) versus the related A(1) and A(3) subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.

Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 A rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 A rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at http://blaster.docking.org .

Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 A rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 A rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at http://blaster.docking.org .