SNAr or Sulfonylation? Chemoselective Amination of Halo(het)arene Sulfonyl Halides for Synthetic Applications and Ultralarge Compound Library Design

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 SNAr-reactive sulfonyl halides typically undergo sulfonamide synthesis during the first step; the second amination is also possible provided that the SNAr-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).

Large library docking for cannabinoid-1 receptor agonists with reduced side effects

Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

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.

Advancing molecular graphs with descriptors for the prediction of chemical reaction yields

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.

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

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.

A stereochemical journey around spirocyclic glutamic acid analogs

A practical divergent synthetic approach is reported for the library of regio- and stereoisomers of glutamic acid analogs built on the spiro[3.3]heptane scaffold. Formation of the spirocyclic scaffold was achieved starting from a common precursor - an O-silylated 2-(hydroxymethyl)cyclobutanone derivative. Its olefination required using the titanium-based Tebbe protocol since the standard Wittig reaction did not work with this particular substrate. The construction of the second cyclobutane ring of the spirocyclic system was achieved through either subsequent dichloroketene addition or Meinwald oxirane rearrangement as the key synthetic steps, depending on the substitution patterns in the target compounds (1,6- or 1,5-, respectively). Further modified Strecker reaction of the resulting racemic spirocyclic ketones with the Ellman's sulfinamide as a chiral auxiliary had low to moderate diastereoselectivity; nevertheless, all stereoisomers were isolated in pure form via chromatographic separation, and their absolute configuration was confirmed by X-ray crystallography. Members of the library were tested for the inhibitory activity against H. pylori glutamate racemase.

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

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 libraries⁷, 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 CB₂/CB₁ 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.

Multigram Synthesis of Advanced 6,6-Difluorospiro[3.3]heptane-derived Building Blocks

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 parallel synthesis of 1,3,5-trisubstituted 1,2,4-triazoles

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 sp³-enriched compounds can be produced. This makes the proposed approach a valuable tool in medicinal chemistry.

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

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.

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A strategy for ultra-large readily accessible (REAL) compound libraries is described

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Pre-validated two- or three-step three-component reaction sequences are used

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A 29-billion chemical space with ∼80% synthesis success rate has been easily obtained

An open-source drug discovery platform enables ultra-large virtual screens

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. 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 screened2. 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 (Kd) = 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.

Supreme activity of gramicidin S against resistant, persistent and biofilm cells of staphylococci and enterococci

Three promising antibacterial peptides were studied with regard to their ability to inhibit the growth and kill the cells of clinical strains of Staphylococcus aureus, Enterococcus faecalis and Enterococcus faecium. The multifunctional gramicidin S (GS) was the most potent, compared to the membranotropic temporin L (TL), being more effective than the innate-defence regulator IDR-1018 (IDR). These activities, compared across 16 strains as minimal bactericidal and minimal inhibitory concentrations (MIC), are independent of bacterial resistance pattern, phenotype variations and/or biofilm-forming potency. For S. aureus strains, complete killing is accomplished by all peptides at 5 × MIC. For E. faecalis strains, only GS exhibits a rapid bactericidal effect at 5 × MIC, while TL and IDR require higher concentrations. The biofilm-preventing activities of all peptides against the six strains with the largest biofilm biomass were compared. GS demonstrates the lowest minimal biofilm inhibiting concentrations, whereas TL and IDR are consistently less effective. In mature biofilms, only GS completely kills the cells of all studied strains. We compare the physicochemical properties, membranolytic activities, model pharmacokinetics and eukaryotic toxicities of the peptides and explain the bactericidal, antipersister and antibiofilm activities of GS by its elevated stability, pronounced cell-penetration ability and effective utilization of multiple modes of antibacterial action.