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Enzymes fold into unique three-dimensional structures, which underlie their remarkable catalytic properties. The requirement to adopt a stable, folded conformation is likely to contribute to their relatively large size (>10,000 Da). However, much shorter p

Positive allosteric modulator (PAM) drugs enhance the activation of the calcium-sensing receptor (CaSR) and suppress parathyroid hormone (PTH) secretion. Unfortunately, these hyperparathyroidism-treating drugs can induce hypocalcemia and arrhythmias. Seeking improved modulators, we docked libraries of 2.7 million and 1.2 billion molecules against the CaSR structure. The billion-molecule docking found PAMs with a 2.7-fold higher hit rate than the million-molecule library, with hits up to 37-fold more potent. Structure-based optimization led to nanomolar leads. In ex vivo organ assays, one of these PAMs was 100-fold more potent than the standard of care, cinacalcet, and reduced serum PTH levels in mice without the hypocalcemia typical of CaSR drugs. As determined from cryo-electron microscopy structures, the PAMs identified here promote CaSR conformations that more closely resemble the activated state than those induced by the established drugs.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns., (© 2024 Wiley-VCH GmbH.)

TRIM7 is a ubiquitin E3 ligase with key regulatory functions, mediating viral infection, tumor biology, innate immunity, and cellular processes, such as autophagy and ferroptosis. It contains a PRYSPRY domain that specifically recognizes degron sequences containing C-terminal glutamine. Ligands that bind to the TRIM7 PRYSPRY domain may have applications in the treatment of viral infections, as modulators of inflammation, and in the design of a new class of PROTACs (PROteolysis TArgeting Chimeras) that mediate the selective degradation of therapeutically relevant proteins (POIs). Here, we developed an assay toolbox for the comprehensive evaluation of TRIM7 ligands. Using TRIM7 degron sequences together with a structure-based design, we developed the first series of peptidomimetic ligands with low micromolar affinity. The terminal carboxylate moiety was required for ligand activity but prevented cell penetration. A prodrug strategy using an ethyl ester resulted in enhanced permeability, which was evaluated using confocal imaging.

Parallel Groebke-Blackburn-Bienaymé reaction was evaluated as a source of multimillion chemically accessible chemical space. Two most popular classical protocols involving the use of Sc(OTf) 3 and TsOH as the catalysts were tested on a broad substrate scope, and prevalence of the first method was clearly demonstrated. Furthermore, the scope and limitations of the procedure were established. A model 790-member library was obtained with 85% synthesis success rate. These results were used to generate a 271-Mln. readily accessible (REAL) heterocyclic chemical space mostly containing unique chemotypes, which was confirmed by comparative analysis with commercially available compound collections. Meanwhile, this chemical space contained 432 compounds that already showed biological activity according to the ChEMBL database., (Copyright © 2024, Govor et al.)

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ 2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them., Competing Interests: BKS is a founder of Epiodyne, Inc, BlueDolphin, LLC, and Deep Apple Therapeutics, Inc., serves on the SAB of Schrodinger LLC and of Vilya Therapeutics, on the SRB of Genentech, and consults for Hyku Therapeutics. JJI co-founded Deep Apple Therapeutics Inc. and BlueDolphin LLC. JSF is a consultant for, has equity in, and receives research support from Relay Therapeutics.

G protein-coupled receptor G2A was postulated to be a promising target for the development of new therapeutics in neuropathic pain, acute myeloid leukemia, and inflammation. However, there is still a lack of potent, selective, and drug-like G2A agonists to be used as a chemical tool or as the starting matter for the development of drugs. In this work, we present the discovery and structure-activity relationship elucidation of a new potent and selective G2A agonist scaffold. Systematic optimization resulted in (3-(pyridin-3-ylmethoxy)benzoyl)- d -phenylalanine (T-10418) exhibiting higher potency than the reference and natural ligand 9-HODE and high selectivity among G protein-coupled receptors. With its favorable activity, a clean selectivity profile, excellent solubility, and high metabolic stability, T-10418 qualifies as a pharmacological tool to investigate the effects of G2A activation.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery., Competing Interests: Declaration of interests B.K.S. and P.G. are founders of Epiodyne. B.K.S. is a co-founder of BlueDolphin and Deep Apple Therapeutics, as is J.J.I., and serves on the SRB of Genentech and the SABs of Vilya Therapeutics and Umbra Therapeutics and consults for Great Point Ventures and Levator Therapeutics. A patent on the discovery of positive and negative allosteric regulators for CFTR has been filed. The authors declare no other competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The proline biosynthetic enzyme Δ 1 -pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) is one of the most consistently upregulated enzymes across multiple cancer types and central to the metabolic rewiring of cancer cells. Herein, we describe a fragment-based, structure-first approach to the discovery of PYCR1 inhibitors. Thirty-seven fragment-like carboxylic acids in the molecular weight range of 143-289 Da were selected from docking and then screened using X-ray crystallography as the primary assay. Strong electron density was observed for eight compounds, corresponding to a crystallographic hit rate of 22%. The fragments are novel compared to existing proline analog inhibitors in that they block both the P5C substrate pocket and the NAD(P)H binding site. Four hits showed inhibition of PYCR1 in kinetic assays, and one has lower apparent IC 50 than the current best proline analog inhibitor. These results show proof-of-concept for our inhibitor discovery approach and provide a basis for fragment-to-lead optimization.

The chemoselectivity of halo(het)arene sulfonyl halide aminations is studied thoroughly under parallel synthesis conditions, and the scope and limitations of the method are established. It is shown that S N Ar-reactive sulfonyl halides typically undergo sulfonamide synthesis during the first step; the second amination is also possible provided that the S N Ar-active center is sufficiently reactive. On the contrary, sulfonyl fluorides bearing an arylating moiety undergo selective transformation at the latter reactive center under proper control. Further sulfur-fluoride exchange (SuFEx) is also possible, which can be especially valuable for some sulfonyl halide classes. The developed two-step parallel double amination protocol provides access to a 6.67-billion compound synthetically tractable REAL-type chemical space (76% expected synthesis success rate).

The lipid prostaglandin E 2 (PGE 2 ) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice., (© 2023. The Author(s).)

Advances in chemoproteomic technology have revealed covalent interactions between small molecules and protein nucleophiles, primarily cysteine, on a proteome-wide scale. Most chemoproteomic screening approaches are indirect, relying on competition between electrophilic fragments and a minimalist electrophilic probe with inherently limited proteome coverage. Here we develop a chemoproteomic platform for direct electrophile-site identification based on enantiomeric pairs of clickable arylsulfonyl fluoride probes. Using stereoselective site modification as a proxy for ligandability in intact cells, we identify 634 tyrosines and lysines within functionally diverse protein sites, liganded by structurally diverse probes. Among multiple validated sites, we discover a chiral probe that modifies Y228 in the MYC binding site of the epigenetic regulator WDR5, as revealed by a high-resolution crystal structure. A distinct chiral probe stimulates tumour cell phagocytosis by covalently modifying Y387 in the recently discovered immuno-oncology target APMAP. Our work provides a deep resource of ligandable tyrosines and lysines for the development of covalent chemical probes., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis prediction, but current models have failed to generalize to large literature datasets. To understand the causes and inspire future design, we systematically benchmarked the yield prediction task. We carefully curated and augmented a literature dataset of 41 239 amide coupling reactions, each with information on reactants, products, intermediates, yields, and reaction contexts, and provided 3D structures for the molecules. We calculated molecular features related to 2D and 3D structure information, as well as physical and electronic properties. These descriptors were paired with 4 categories of machine learning methods (linear, kernel, ensemble, and neural network), yielding valuable benchmarks about feature and model performance. Despite the excellent performance on a high-throughput experiment (HTE) dataset ( R 2 around 0.9), no method gave satisfactory results on the literature data. The best performance was an R 2 of 0.395 ± 0.020 using the stack technique. Error analysis revealed that reactivity cliff and yield uncertainty are among the main reasons for incorrect predictions. Removing reactivity cliffs and uncertain reactions boosted the R 2 to 0.457 ± 0.006. These results highlight that yield prediction models must be sensitive to the reactivity change due to the subtle structure variance, as well as be robust to the uncertainty associated with yield measurements., Competing Interests: The authors declare no conflicts., (This journal is © The Royal Society of Chemistry.)

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (M Pro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC 50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of M Pro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like M Pro versus other targets where it has been more successful, and versus other structure-based techniques against M Pro itself., (© 2023 The Protein Society.)

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 IC 50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC 50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC 50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC 50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC 50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

Retinoic acid receptor-related orphan receptor γt (RORγt) is a nuclear receptor that is expressed in a variety of tissues and is a potential drug target for the treatment of inflammatory and auto-immune diseases, metabolic diseases, and resistant cancer types. We herein report the discovery of 2,3 derivatives of 4,5,6,7-tetrahydro-benzothiophene modulators of RORγt. We also report the solubility in acidic/neutral pH, mouse/human/dog/rat microsomal stability, Caco-2, and MDR1-MDCKII permeabilities of a set of these derivatives. For this group of modulators, inverse agonism by steric clashes and push-pull mechanisms induce greater instability to protein conformation compared to agonist lock hydration. Independent of the two mechanisms, we observed a basal modulatory activity of the tested 2,3 derivatives of 4,5,6,7-tetrahydro-benzothiophene toward RORγt due to the interactions with the Cys320-Glu326 and Arg364-Phe377 hydrophilic regions. The drug discovery approach reported in the current study can be employed to discover modulators of nuclear receptors and other globular protein targets.

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.

Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high-yielding reactions and score synthesis routes, saving time and reagents. This study suggests a novel graph neural network architecture for chemical yield prediction. The network combines structural information about participants of the transformation as well as molecular and reaction-level descriptors. It works with incomplete chemical reactions and generates reactants-product atom mapping. We show that the network benefits from advanced information by comparing it with several machine learning models and molecular representations. Models included logistic regression, support vector machine, CatBoost, and Bidirectional Encoder Representations from Transformers. Molecular representations included extended-connectivity fingerprints, Morgan fingerprints, SMILESVec embeddings, and textual. Classification and regression objectives were assessed for each model and feature set. The goal of each classification model was to separate zero- and non-zero-yielding reactions. The models were trained and evaluated on a proprietary dataset of 10 reaction types. Also, the models were benchmarked on two public single reaction type datasets. The study was supplemented with analysis of data, results, and errors, as well as the impact of steric factors, side reactions, isolation, and purification efficiency. The supplementary code is available at https://github.com/SoftServeInc/yield-paper., (© 2022 Wiley Periodicals LLC.)

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

The unprovoked Russian invasion has created considerable challenges for Ukrainian science. In this article, we discuss actions needed to support and rebuild Ukrainian science and educational systems. The proposed actions take into account past Ukrainian scientific achievements including developments in organic chemistry.

Fragment-based drug discovery (FBDD) has successfully led to approved therapeutics for challenging and "undruggable" targets. In the context of FBDD, we introduce a novel, multidisciplinary method to identify active molecules from purchasable chemical space. Starting from four small-molecule fragment complexes of protein kinase A (PKA), a template-based docking screen using Enamine's multibillion REAL Space was performed. A total of 93 molecules out of 106 selected compounds were successfully synthesized. Forty compounds were active in at least one validation assay with the most active follow-up having a 13,500-fold gain in affinity. Crystal structures for six of the most promising binders were rapidly obtained, verifying the binding mode. The overall success rate for this novel fragment-to-hit approach was 40%, accomplished in only 9 weeks. The results challenge the established fragment prescreening paradigm since the standard industrial filters for fragment hit identification in a thermal shift assay would have missed the initial fragments.

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches., (© 2022. The Author(s).)

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α 2A -adrenergic receptor (α 2A AR), seeking new α 2A AR agonists chemotypes that lack the sedation conferred by known α 2A AR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential G i and G o signaling. Experimental structures of α 2A AR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC 50 ) of 52 nanomolar] and two analogs, '7075 and PS75 (EC 50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

Little is known about the mechanisms behind the bistability (memory) of molecular spin transition compounds over broad temperature ranges (>100 K). To address this point, we report on a new discrete Fe II neutral complex [Fe II L 2 ] 0 ( 1 ) based on a novel asymmetric tridentate ligand 2-(5-(3-methoxy-4 H -1,2,4-triazol-3-yl)-6-(1 H -pyrazol-1-yl))pyridine (L). Due to the asymmetric cone-shaped form, in the lattice, the formed complex molecules stack into a one-dimensional (1D) supramolecular chain. In the case of the rectangular supramolecular arrangement of chains in methanolates 1-A and 1-B (both orthorhombic, Pbcn ) differing, respectively, by bent and extended spatial conformations of the 3-methoxy groups (3MeO), a moderate cooperativity is observed. In contrast, the hexagonal-like arrangement of supramolecular chains in polymorph 1-C (monoclinic, P 2 1 / c ) results in steric coupling of the transforming complex species with the peripheral flipping 3MeO group. The group acts as a supramolecular latch, locking the huge geometric distortion of complex 1 and in turn the trigonal distortion of the central Fe II ion in the high-spin state, thereby keeping it from the transition to the low-spin state over a large thermal range. Analysis of the crystal packing of 1-C reveals significantly changing patterns of close intermolecular interactions on going between the phases substantiated by the energy framework analysis. The detected supramolecular mechanism leads to a record-setting robust 105 K wide hysteresis spanning the room temperature region and an atypically large T LIESST relaxation value of 104 K of the photoexcited high-spin state. This work highlights a viable pathway toward a new generation of cleverly designed molecular memory materials.

Ukrainian companies occupy an important niche in the global drug discovery process; however, before the Russian invasion, the role of Ukraine was not obvious. The biggest Ukrainian fine chemical supplier, Enamine Ltd, had to stop operation for more than a month, which significantly affected various early-stage drug discovery projects. The role of Enamine in drug discovery and the company's past and future in the context of the Russian invasion are described in this Viewpoint., Competing Interests: The authors declare no competing financial interest., (© 2022 American Chemical Society.)

The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.

An implementation of the three-component one-pot approach to unsymmetrical 1,3,5-trisubstituted-1,2,4-triazoles into combinatorial chemistry is described. The procedure is based on the coupling of amidines with carboxylic acids and subsequent cyclization with hydrazines. After the preliminary assessment of the reagent scope, the method had 81% success rate in parallel synthesis. It was shown that over a billion-sized chemical space of readily accessible ("REAL") compounds may be generated based on the proposed methodology. Analysis of physicochemical parameters shows that the library contains significant fractions of both drug-like and "beyond-rule-of-five" members. More than 10 million of accessible compounds meet the strictest lead-likeness criteria. Additionally, 195 Mln of sp 3 -enriched compounds can be produced. This makes the proposed approach a valuable tool in medicinal chemistry., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures 1-4 and ultra-large libraries of virtual compounds 5,6 . However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries 7 , new approaches to compound screening are needed 8,9 . Here we introduce a modular synthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (K i ) = 0.9 nM) and CB 2 /CB 1 selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

A convenient methodology for constructing 6,6-difluorospiro[3.3]heptane scaffold - a conformationally restricted isostere of gem -difluorocycloalkanes - is developed. Alarge array of novel 2-mono- and 2,2-bifunctionalized difluorospiro[3.3]heptane building blocks was obtained through the convergent synthesis strategy using a common synthetic precursor - 1,1-bis(bromomethyl)-3,3-difluorocyclobutane. The target compounds and intermediates were prepared by short reaction sequences (6-10 steps) on multigram scale (up to 0.47 kg).

One-pot intramolecular cyclization of novel sp 3 -enriched cyanoalkylsulfonyl fluorides into spirocyclic β- or γ-sultams is disclosed. The method relies on nitrile group reduction followed by sulfonylation of amino group thus formed upon mild conditions (NaBH 4 , NiCl 2 ·6H 2 O in MeOH). Cyclization proceeds smoothly with considerable efficiency (48-84%, 10 examples) on up to 30 g scale. The cyanoalkylsulfonyl fluoride intermediates can be obtained via S-nucleophilic substitution in β-functionalized alkanenitriles or double alkylation of α-alkylthioacetonitrile, followed by oxidative chlorination with Cl 2 and further reaction with KHF 2 . The title mono- and bifunctional sultams are advanced sp 3 -enriched building blocks for drug discovery and organic synthesis providing novel substitution patterns and frameworks mimicking saturated nitrogen heterocycles such as pyrrolidine/pyrrolidone.

The σ 2 receptor has attracted intense interest in cancer imaging 1 , psychiatric disease 2 , neuropathic pain 3-5 and other areas of biology 6,7 . Here we determined the crystal structure of this receptor in complex with the clinical candidate roluperidone 2 and the tool compound PB28 8 . These structures templated a large-scale docking screen of 490 million virtual molecules, of which 484 compounds were synthesized and tested. We identified 127 new chemotypes with affinities superior to 1 μM, 31 of which had affinities superior to 50 nM. The hit rate fell smoothly and monotonically with docking score. We optimized three hits for potency and selectivity, and achieved affinities that ranged from 3 to 48 nM, with up to 250-fold selectivity versus the σ 1 receptor. Crystal structures of two ligands bound to the σ 2 receptor confirmed the docked poses. To investigate the contribution of the σ 2 receptor in pain, two potent σ 2 -selective ligands and one potent σ 1 /σ 2 non-selective ligand were tested for efficacy in a mouse model of neuropathic pain. All three ligands showed time-dependent decreases in mechanical hypersensitivity in the spared nerve injury model 9 , suggesting that the σ 2 receptor has a role in nociception. This study illustrates the opportunities for rapid discovery of in vivo probes through structure-based screens of ultra large libraries, enabling study of underexplored areas of biology., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

A series of 2-(1 H -1,2,4-triazol-3-yl)acetates, as well as 4-mono- and 4,4-disubstituted 5-amino-2,4-dihydro-3 H -pyrazol-3-ones (including spirocyclic derivatives) have been synthesized using the Pinner reaction strategy. α-Mono- and α,α-disubstituted ethyl cyanoacetates were converted into the corresponding carboxyimidate salts that served as the key intermediates. Their further reaction with formylhydrazide or hydrazine hydrate provided triazolylacetates or aminopyrazolones (including spirocyclic derivatives), depending on the structure of the starting Pinner salt and the nature of the nucleophile. The scope and limitations of the developed synthetic method have been established.

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.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

An approach to the generation of ultra-large chemical libraries of readily accessible ("REAL") compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation - deprotection - acylation of monoprotected diamines or amide formation - click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and "beyond rule-of-five" members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million., Competing Interests: The authors declare no competing interests., (© 2020 The Author(s).)

With the aim of circumventing the adverse cis / trans -isomerization of combretastatin A4 (CA4), a naturally occurring tumor-vascular disrupting agent, we designed novel CA4 analogs bearing 1,3-cyclobutane moiety instead of the cis -stilbene unit of the parent compound. The corresponding cis and trans cyclobutane-containing derivatives were prepared as pure diastereomers. The structure of the target compounds was confirmed by X-ray diffraction study. The title compounds were evaluated for their cytotoxic properties in human cancer cell lines HepG2 (hepatocarcinoma) and SK-N-DZ (neuroblastoma), and the overall activity was found in micromolar range. Molecular docking studies and molecular dynamics simulation within the colchicine binding site of tubulin were in good agreement with the obtained cytotoxicity data.

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

The neuromodulator melatonin synchronizes circadian rhythms and related physiological functions through the actions of two G-protein-coupled receptors: MT 1 and MT 2 . Circadian release of melatonin at night from the pineal gland activates melatonin receptors in the suprachiasmatic nucleus of the hypothalamus, synchronizing the physiology and behaviour of animals to the light-dark cycle 1-4 . The two receptors are established drug targets for aligning circadian phase to this cycle in disorders of sleep 5,6 and depression 1-4,7-9 . Despite their importance, few in vivo active MT 1 -selective ligands have been reported 2,8,10-12 , hampering both the understanding of circadian biology and the development of targeted therapeutics. Here we docked more than 150 million virtual molecules to an MT 1 crystal structure, prioritizing structural fit and chemical novelty. Of these compounds, 38 high-ranking molecules were synthesized and tested, revealing ligands with potencies ranging from 470 picomolar to 6 micromolar. Structure-based optimization led to two selective MT 1 inverse agonists-which were topologically unrelated to previously explored chemotypes-that acted as inverse agonists in a mouse model of circadian re-entrainment. Notably, we found that these MT 1 -selective inverse agonists advanced the phase of the mouse circadian clock by 1.3-1.5 h when given at subjective dusk, an agonist-like effect that was eliminated in MT 1 - but not in MT 2 -knockout mice. This study illustrates the opportunities for modulating melatonin receptor biology through MT 1 -selective ligands and for the discovery of previously undescribed, in vivo active chemotypes from structure-based screens of diverse, ultralarge libraries.

A comprehensive study on the synthesis of 5-fluoroalkyl-substituted isoxazoles starting from functionalized halogenoximes is reported. One-pot metal-free [3 + 2] cycloaddition of CF 3 -substituted alkenes and halogenoximes bearing ester, bromo, chloromethyl, and protected amino groups was developed for the preparation of 5-trifluoromethylisoxazoles. The target 3,5-disubstituted derivatives were obtained in a regioselective manner in good to excellent yield on up to 130 g scale. 5-Fluoromethyl- and 5-difluoromethylisoxazoles were synthesized by late-stage deoxofluorination of the corresponding 5-hydroxymethyl or 5-formyl derivatives, respectively, in turn prepared via metal-free cycloaddition of halogenoximes and propargylic alcohol. An alternative approach based on nucleophilic substitution in 5-bromomethyl derivatives was found to be more convenient for the preparation of 5-fluoromethylisoxazoles. Reaction of isoxazole-5-carbaldehydes with the Ruppert-Prakash reagent was used for the preparation of (β,β,β-trifluoro-α-hydroxyethyl)isoxazoles. Utility of described approaches was shown by multigram preparation of side-chain functionalized mono-, di-, and trifluoromethylisoxazoles, for example, fluorinated analogues of ABT-418 and ESI-09.

A facile synthetic route toward either 3- or 5-fluoroalkyl-substituted isoxazoles or pyrazoles containing an additional functionalization site was developed and applied on a multigram scale. The elaborated approach extends the scope of fluoroalkyl substituents for introduction into the heterocyclic moiety, and uses convenient transformations of the side chain for incorporation of fluoroalkyl-substituted azoles into the structures of biologically active molecules. The utility of the obtained building blocks for isosteric replacement of alkyl-substituted isoxazole and pyrazole was shown by the synthesis of fluorinated Isocarboxazid and Mepiprazole analogues.

Two protocols for the combinatorial synthesis of 5-(dialkylamino)tetrazoles were developed. The best success rate (67%) was shown by the method that used primary and secondary amines, 2,2,2-trifluoroethylthiocarbamate, and sodium azide as the starting reagents. The key steps included the formation of unsymmetrical thiourea, subsequent alkylation with 1,3-propane sultone and cyclization with azide anion. A 559-member aminotetrazole library was synthesized by this approach; the overall readily accessible (REAL) chemical space covered by the method exceeded 7 million feasible compounds.

We introduce SAR-by-Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolecular hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand- and structure-based approaches in hit generation and lead optimization stages., Competing Interests: The work herein is a joint collaboration between Enamine or BioSolveIT of which some of the authors are employees; the chemical space navigation software is available for free for testing but commercially available for longer term usage from BioSolveIT. Enamine offers the compounds for purchase. The funding parties had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Minimalist enzymes designed to catalyze model reactions provide useful starting points for creating catalysts for practically important chemical transformations. We have shown that Kemp eliminases of the AlleyCat family facilitate conversion of leflunomide (an immunosupressor pro-drug) to its active form teriflunomide with outstanding rate enhancement (nearly four orders of magnitude) and catalytic proficiency (more than seven orders of magnitude) without any additional optimization. This remarkable activity is achieved by properly positioning the substrate in close proximity to the catalytic glutamate with very high pK a .

The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure-activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Over recent years, an industry of compound suppliers has grown to provide drug discovery with screening compounds: it is estimated that there are over 16 million compounds available from these sources. Here, we review the chemical space covered by suppliers' compound libraries (SCL) in terms of compound physicochemical properties, novelty, diversity, and quality. We examine the feasibility of compiling high-quality vendor-based libraries avoiding complicated, expensive compound management activity, and compare the resulting libraries to the ChEMBL data set. We also consider how vendors have responded to the evolving requirements for drug discovery., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)