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Human African trypanosomiasis, or sleeping sickness, is a neglected tropical disease caused by Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense and is invariably fatal unless treated. Current therapies present limitations in their application, parasite resistance, or require further clinical investigation for wider use. Our work, informed by previous findings, presents novel 4-[4-(4-methylpiperazin-1-yl)phenyl]-6-arylpyrimidine derivatives with promising antitrypanosomal activity. In particular, 32 exhibits an in vitro EC50 value of 0.5 µM against Trypanosoma brucei rhodesiense, and analogues 29, 30 and 33 show antitrypanosomal activities in the

Kavaratamide A (1), a new linear lipodepsipeptide possessing an unusual isopropyl-O-methylpyrrolinone moiety, was discovered from the tropical marine filamentous cyanobacterium Moorena bouillonii collected from Kavaratti, India. A comparative chemogeographic analysis of M. bouillonii collected from six different geographical regions led to the prioritized isolation of this metabolite from India as distinctive among our data sets. AI-based structure annotation tools, including SMART 2.1 and DeepSAT, accelerated the structure elucidation by providing useful structural clues, and the full planar structure was elucidated based on comprehensive HRMS, MS/MS fragmentation, and NMR data interpretation. Subsequently, the absolute configuration of 1 was determined using advanced Marfey's analysis, modified Mosher's ester derivatization, and chiral-phase HPLC. The structures of kavaratamides B (2) and C (3) are proposed based on a detailed analysis of their MS/MS fragmentations. The biological activity of kavaratamide A was also investigated and found to show moderate cytotoxicity to the D283-medullablastoma cell line.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.

Marine-derived Streptomyces have long been recognized as a source of novel, pharmaceutically relevant natural products. Among these bacteria, the MAR4 clade within the genus Streptomyces has been identified as metabolically rich, yielding over 93 different compounds to date. MAR4 strains are particularly noteworthy for the production of halogenated hybrid isoprenoid natural products, a relatively rare class of bacterial metabolites that possess a wide range of biological activities. MAR4 genomes are enriched in vanadium haloperoxidase and prenyltransferase genes, thus accounting for the production of these compounds. Functional characterization of the enzymes encoded in MAR4 genomes has advanced our understanding of halogenated, hybrid isoprenoid biosynthesis. Despite the exceptional biosynthetic capabilities of MAR4 bacteria, the large body of research they have stimulated has yet to be compiled. Here we review 35 years of natural product research on MAR4 strains and update the molecular diversity of this unique group of bacteria.

Greenblatt and his team have unveiled vertebral skeletal stem cells (vSSCs) as a critical player in the landscape of bone metastasis. This commentary delves into the transformative discoveries surrounding vSSCs, emphasizing their distinct role in bone metastasis compared to other stem cell lineages. We illuminate the unique properties and functions of vSSCs, which may account for the elevated susceptibility of vertebral bones to metastatic invasion. Furthermore, we explore the exciting therapeutic horizons opened by this newfound understanding. These include potential interventions targeting vSSCs, modulation of associated signaling pathways, and broader implications for the treatment and management of bone metastasis. By shedding light on these game-changing insights, we hope to pave the way for novel strategies that could revolutionize the prognosis and treatment landscape for cancer patients with metastatic bone disease.

Cancer immunotherapy has emerged as a promising strategy for the treatment of cancer, with the tumor microenvironment (TME) playing a pivotal role in modulating the immune response. CD47, a cell surface protein, has been identified as a crucial regulator of the TME and a potential therapeutic target for cancer therapy. However, the precise functions and implications of CD47 in the TME during immunotherapy for cancer patients remain incompletely understood. This comprehensive review aims to provide an overview of CD47's multifaced role in TME regulation and immune evasion, elucidating its impact on various types of immunotherapy outcomes, including checkpoint inhibitors and CAR T-cell therapy. Notably, CD47-targeted therapies offer a promising avenue for improving cancer treatment outcomes, especially when combined with other immunotherapeutic approaches. The review also discusses current and potential CD47-targeted therapies being explored for cancer treatment and delves into the associated challenges and opportunities inherent in targeting CD47. Despite the demonstrated effectiveness of CD47-targeted therapies, there are potential problems, including unintended effects on healthy cells, hematological toxicities, and the development if resistance. Consequently, further research efforts are warranted to fully understand the underlying mechanisms of resistance and to optimize CD47-targeted therapies through innovative combination approaches, ultimately improving cancer treatment outcomes. Overall, this comprehensive review highlights the significance of CD47 as a promising target for cancer immunotherapy and provides valuable insight into the challenges and opportunities in developing effective CD47-targeted therapies for cancer treatment.

Series of 1,3-disubstituted ureas and diadamantyl disubstituted diureas with fluorinated and chlorinated adamantane residues were shown to inhibit human soluble epoxide hydrolase (sEH) with inhibition potency ranging from 40 pM to 9.2 nM. The measured IC50 values for some molecules were below the accuracy limit of the existing in vitro assays. Such an increase in activity was achieved by minimal structural modifications to the molecules of known inhibitors, including 4-[trans-4-(1-adamantylcarbamoylamino)cyclohexyl]oxybenzoic acid. For the chlorinated homologue of the latter the sharp jump in inhibitory activity can be (according to molecular dynamics data) the result of interactions - Cl-π interaction. Considering the extremely high inhibitory activity, acceptable solubility and partial blockage of metabolically sensitive centres in their structures, some compounds are of interest for further in vivo biotesting.

Schistosomiasis is a disease affecting >200 million people worldwide, but its treatment relies on a single agent, praziquantel. To investigate new avenues for schistosomiasis control, we have conducted the first systematic analysis of bromodomain-containing proteins (BCPs) in a causative species, Schistosoma mansoni. Having identified 29 putative bromodomains (BRDs) in 22 S. mansoni proteins, we selected SmBRD3, a tandem BRD-containing BCP that shows high similarity to the human bromodomain and extra terminal domain (BET) family, for further studies. Screening 697 small molecules identified the human BET BRD inhibitor I-BET726 as a ligand for SmBRD3. An X-ray crystal structure of I-BET726 bound to the second BRD of SmBRD3 [SmBRD3(2)] enabled rational design of a quinoline-based ligand (15) with an ITC Kd = 364 ± 26.3 nM for SmBRD3(2). The ethyl ester pro-drug of compound 15 (compound 22) shows substantial effects on sexually immature larval schistosomula, sexually mature adult worms, and snail-infective miracidia in ex vivo assays.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

The C-diazeniumdiolate group in the amino acid graminine is emerging as a new microbially produced Fe(III) coordinating ligand in siderophores, which is photoreactive. While the few siderophores reported from this class have only been isolated from soil-associated microbes, here we report the first C-diazeniumdiolate siderophores tistrellabactins A and B, isolated from the bioactive marine-derived strain Tistrella mobilis KA081020-065. The structural characterization of the tistrellabactins reveals unique biosynthetic features including an NRPS module iteratively loading glutamine residues and a promiscuous adenylation domain yielding either tistrellabactin A with an asparagine residue or tistrellabactin B with an aspartic acid residue at analogous positions. Beyond the function of scavenging Fe(III) for growth, these siderophores are photoreactive upon irradiation with UV light, releasing the equivalent of nitric oxide (NO) and an H atom from the C-diazeniumdiolate group. Fe(III)-tistrellabactin is also photoreactive, with both the C-diazeniumdiolate and the β-hydroxyaspartate residues undergoing photoreactions, resulting in a photoproduct without the ability to chelate Fe(III).

Many anticancer drugs exhibit high systemic off-target toxicities causing severe side effects. Peptide-drug conjugates (PDCs) that target tumor-specific receptors such as integrin αvβ6 are emerging as powerful tools to overcome these challenges. The development of an integrin αvβ6-selective PDC was achieved by combining the therapeutic efficacy of the cytotoxic drug monomethyl auristatin E with the selectivity of the αvβ6-binding peptide (αvβ6-BP) and with the ability of positron emission tomography (PET) imaging by copper-64. The [64Cu]PDC-1 was produced efficiently and in high purity. The PDC exhibited high human serum stability, integrin αvβ6-selective internalization, cell binding, and cytotoxicity. Integrin αvβ6-selective tumor accumulation of the [64Cu]PDC-1 was visualized with PET-imaging and corroborated by biodistribution, and [64Cu]PDC-1 showed promising in vivo pharmacokinetics. The [natCu]PDC-1 treatment resulted in prolonged survival of mice bearing αvβ6 (+) tumors (median survival: 77 days, vs αvβ6 (-) tumor group 49 days, and all other control groups 37 days).

Fluorinated alcohols and phenols are potentially useful as bioisosteres of the carboxylic acid functional group. To enable a direct comparison of the properties of fluorinated carboxylic acid surrogates with those of other commonly used, non-fluorinated bioisosteres, we conducted a structure-property relationship (SPR) study based on matched molecular pair (MMP) analyses. A series of representative examples have been characterized by experimentally determining physicochemical properties, such as acidity (pKa), lipophilicity (logD7.4), and permeability (PAMPA). The results presented can help estimate the relative changes in physicochemical properties that may be attainable by replacing the carboxylic acid functional group with fluorine containing surrogate structures.

In continuation of our previous efforts for the development of potent small molecules against brain cancer, herein we synthesized seventeen new compounds and tested their anti-gliomapotential against established glioblastoma cell lines, namely, D54MG, U251, and LN-229 as well as patient derived cell lines (DB70 and DB93). Among them, the carboxamide derivatives, BT-851 and BT-892 were found to be the most active leads in comparison to our established hit compound BT#9.The SAR studies of our hit BT#9 compound resulted in the development of two new lead compounds by hit to lead strategy. The detailed biological studies are currently underway. The active compounds could possibly act as template for the future development of newer anti-glioma agents.

Heteroaromatic stacking interactions are important in drug binding, supramolecular chemistry, and materials science, making protein-ligand model systems of these interactions of considerable interest. Here we studied 30 congeneric ligands that each present a distinct heteroarene for stacking between tyrosine residues at the dimer interface of procaspase-6. Complex X-ray crystal structures of 10 analogs showed that stacking geometries were well conserved, while high-accuracy computations showed that heteroarene stacking energy was well correlated with predicted overall ligand binding energies. Empirically determined KD values in this system thus provide a useful measure of heteroarene stacking with tyrosine. Stacking energies are discussed in the context of torsional strain, the number and positioning of heteroatoms, tautomeric state, and coaxial orientation of heteroarene in the stack. Overall, this study provides an extensive data set of empirical and high-level computed binding energies in a versatile new protein-ligand system amenable to studies of other intermolecular interactions.

Molecular dynamics simulation is an indispensable tool for understanding the collective behavior of atoms and molecules and the phases they form. Statistical mechanics provides accurate routes for predicting macroscopic properties as time-averages over visited molecular configurations - microstates. However, to obtain convergence, we need a sufficiently long record of visited microstates, which translates to the high-computational cost of the molecular simulations. In this work, we show how to use a point cloud-based deep learning strategy to rapidly predict the structural properties of liquids from a single molecular configuration. We tested our approach using three homogeneous liquids with progressively more complex entities and interactions: Ar, NO, and H2O under varying pressure and temperature conditions within the liquid state domain. Our deep neural network architecture allows rapid insight into the liquid structure, here probed by the radial distribution function, and can be used with molecular/atomistic configurations generated by either simulation, first-principle, or experimental methods.

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.

Marinolides A and B, two new 24- and 26-membered bacterial macrolactones, were isolated from the marine-derived actinobacterium AJS-327 and their stereostructures initially assigned by bioinformatic data analysis. Macrolactones typically possess complex stereochemistry, the assignments of which have been one of the most difficult undertakings in natural products chemistry, and in most cases, the use of X-ray diffraction methods and total synthesis have been the major methods of assigning their absolute configurations. More recently, however, it has become apparent that the integration of bioinformatic data is growing in utility to assign absolute configurations. Genome mining and bioinformatic analysis identified the 97 kb mld biosynthetic cluster harboring seven type I polyketide synthases. A detailed bioinformatic investigation of the ketoreductase and enoylreductase domains within the multimodular polyketide synthases, coupled with NMR and X-ray diffraction data, allowed for the absolute configurations of marinolides A and B to be determined. While using bioinformatics to assign the relative and absolute configurations of natural products has high potential, this method must be coupled with full NMR-based analysis to both confirm bioinformatic assignments as well as any additional modifications that occur during biosynthesis.

Highly functionalized skeletons of macrolide natural products gain access to rare spatial arrangements of atoms, where changes in stereochemistry can have a profound impact on the structure and function. Spliceosome modulators present a unique consensus motif, with the majority targeting a key interface within the SF3B spliceosome complex. Our recent preparative-scale synthetic campaign of 17S-FD-895 provided unique access to stereochemical analogues of this complex macrolide. Here, we report on the preparation and systematic activity evaluation of multiple FD-895 analogues. These studies examine the effects of modifications at specific stereocenters within the molecule and highlight future directions for medicinal chemical optimization of spliceosome modulators.

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

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.

A drug discovery and development pipeline is a prolonged and complex process that remains challenging for both computational methods and medicinal chemists and has not been able to be resolved using computational methods. Deep learning has been utilized in various fields and achieved tremendous success in designing novel molecules in the pharmaceutical industry. Herein, we use state-of-the-art techniques to propose a deep neural network, AIMLinker, to rapidly design and generate meaningful drug-like proteolysis targeting chimeras (PROTACs) analogs. The model extracts the structural information from the input fragments and generates linkers to incorporate them. We integrate filters in the model to exclude nondruggable structures guided via protein-protein complexes while retaining molecules with potent chemical properties. The novel PROTACs subsequently pass through molecular docking, taking root-mean-square deviation (RMSD), relative Gibbs free energy (ΔΔGbinding), molecular dynamics (MD) simulation, and free energy perturbation (FEP) calculations as the measurement criteria for testing the robustness and feasibility of the model. The generated novel PROTACs molecules possess similar structural information with superior binding affinity to the binding pockets compared to the existing CRBN-dBET6-BRD4 ternary complexes. We demonstrate the effectiveness of the methodology of leveraging AIMLinker to design novel compounds for PROTACs molecules exhibiting better chemical properties compared to the dBET6 crystal pose.

Early antiviral treatments, including intravenous remdesivir (RDV), reduce hospitalization and severe disease caused by COVID-19. An orally bioavailable RDV analog may facilitate earlier treatment of non-hospitalized COVID-19 patients. Here we describe the synthesis and evaluation of alkyl glyceryl ether phosphodiesters of GS-441524 (RVn), lysophospholipid analogs which allow for oral bioavailability and stability in plasma. Oral treatment of SARS-CoV-2-infected BALB/c mice with 1-O-octadecyl-2-O-benzyl-sn-glyceryl-3-phospho-RVn (60 mg/kg orally, once daily for 5 days starting 12h after infection) reduced lung viral load by 1.5 log10 units versus vehicle at day 2 and to below the limit of detection at day 5. Structure/activity evaluation of additional analogs that have hydrophobic ethers at the sn-2 of glycerol revealed improved in vitro antiviral activity by introduction of a 3-fluoro-4-methoxy-substituted benzyl or a 3- or 4-cyano-substituted benzyl. Collectively, our data support the development of RVn phospholipid prodrugs as oral antiviral agents for prevention and treatment of SARS-CoV-2 infections.

CDC42 GTPases (RHOJ, CDC42, and RHOQ) are overexpressed in multiple tumor types and activate pathways critical for tumor growth, angiogenesis, and metastasis. Recently, we reported the discovery of a novel lead compound, ARN22089, which blocks the interaction of CDC42 GTPases with specific downstream effectors. ARN22089 blocks tumor growth in BRAF mutant mouse melanoma models and patient-derived xenografts (PDXs) in vivo. ARN22089 also inhibits tumor angiogenesis in three-dimensional vascularized microtumor models in vitro. Notably, ARN22089 belongs to a novel class of trisubstituted pyrimidines. Based on these results, we describe an extensive structure-activity relationship of ∼30 compounds centered on ARN22089. We discovered and optimized two novel inhibitors (27, ARN25062, and 28, ARN24928), which are optimal back-up/follow-up leads with favorable drug-like properties and in vivo efficacy in PDX tumors. These findings further demonstrate the potential of this class of CDC42/RHOJ inhibitors for cancer treatment, with lead candidates ready for advanced preclinical studies.

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.

The anion exchanger protein SLC26A3 (down-regulated in adenoma, DRA) is expressed in the luminal membrane of intestinal epithelial cells in colon, where it facilitates the absorption of Cl- and oxalate. We previously identified a 4,8-dimethylcoumarin class of SLC26A3 inhibitors that act from the SLC26A3 cytoplasmic surface, and demonstrated their efficacy in mouse models of constipation and hyperoxaluria. Here, screening of 50,000 new compounds and 1740 chemical analogs of active compounds from the primary screen produced five novel classes of SLC26A3-selective inhibitors (1,3-dioxoisoindoline-amides; N-(5-sulfamoyl-1,3,4-thiadiazol-2-yl)acetamides; thiazolo-pyrimidin-5-ones; 3-carboxy-2-phenylbenzofurans and benzoxazin-4-ones) with IC50 down to 100 nM. Kinetic washout and onset of action studies revealed an extracellular site of action for the thiazolo-pyrimidin-5-one and 3-carboxy-2-phenylbenzofuran inhibitors. Molecular docking computations revealed putative binding sites for these inhibitors. In a loperamide model of constipation in mice, orally administered 7-(2-chloro-phenoxymethyl)-3-phenyl-thiazolo [3,2-a]pyrimidin-5-one (3a) significantly increased stool weight, pellet number and water content. SLC26A3 inhibitors with an extracellular site of action offer the possibility of creating non-absorbable, luminally acting inhibitors with minimal systemic exposure following oral administration. Our findings also suggest that inhibitors of related SLC26 anion transporters with an extracellular site of action might be identified for pharmacological modulation of selected epithelial ion transport processes.

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.

We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.

Soluble epoxide hydrolase (sEH) has been identified as an attractive target for anti-inflammatory drug design in recent years. Picomolar level compound G1 against sEH was obtained by introducing the hydrophilic group homopiperazine and hydrophobic fragment propionyl onto the structure of lead compound A. G1 showed good microsomal stability, a moderate plasma protein binding rate, and good oral bioavailability and was well tolerated in rats. G1 has significant analgesic effects on CFA-induced AIA mice, ameliorated the pancreatic injury in acute pancreatitis induced by l-arginine, reversed pancreatic injury, edema, and neutrophil infiltration, and increased the survival time of C57BL/6 mice in a lipopolysaccharide (LPS)-induced sepsis model. Moreover the expression levels of sEH, COX-2, NOS-2, vascular cell adhesion molecule (VCAM), IL-6, MCP-5, and tumor necrosis factor α (TNF-α) were measured by Western blot or enzyme-linked immunosorbent assay (ELISA), with varying degrees of decrease. These results suggested that G1 is a drug candidate worthy of further evaluation for the treatment of inflammation-induced diseases such as arthritis, acute pancreatitis, and sepsis.

Interest in covalent enzyme inhibitors as therapeutic agents has seen a recent resurgence. Covalent enzyme inhibitors typically possess an organic functional group that reacts with a key feature of the target enzyme, often a nucleophilic cysteine residue. Herein, the application of small, modular ReV complexes as inorganic cysteine-targeting warheads is described. These metal complexes were found to react with cysteine residues rapidly and selectively. To demonstrate the utility of these ReV complexes, their reactivity with SARS-CoV-2-associated cysteine proteases is presented, including the SARS-CoV-2 main protease and papain-like protease and human enzymes cathepsin B and L. As all of these proteins are cysteine proteases, these enzymes were found to be inhibited by the ReV complexes through the formation of adducts. These findings suggest that these ReV complexes could be used as a new class of warheads for targeting surface accessible cysteine residues in disease-relevant target proteins.

Human carbonic anhydrase II (hCAII) is a metalloenzyme essential to critical physiological processes in the body. hCA inhibitors are used clinically for the treatment of indications ranging from glaucoma to epilepsy. Targeted protein degraders have emerged as a promising means of inducing the degradation of disease-implicated proteins by using the endogenous quality control mechanisms of a cell. Here, a series of heterobifunctional degrader candidates targeting hCAII were developed from a simple aryl sulfonamide fragment. Degrader candidates were functionalized to produce either cereblon E3 ubiquitin ligase (CRBN) recruiting proteolysis targeting chimeras (PROTACs) or adamantyl-based hydrophobic tags (HyTs). Screens in HEK293 cells identified two PROTAC small-molecule degraders of hCA. Optimization of linker length and composition yielded a degrader with sub-nanomolar potency and sustained depletion of hCAII over prolonged treatments. Mechanistic studies suggest that this optimized degrader depletes hCAII through the same mechanism as previously reported CRBN-recruiting heterobifunctional degraders.

Marinobazzanan (1), a new bazzanane-type sesquiterpenoid, was isolated from a marine-derived fungus belonging to the genus Acremonium. The chemical structure of 1 was elucidated using NMR and mass spectroscopic data, while the relative configurations were established through the analysis of NOESY data. The absolute configurations of 1 were determined by the modified Mosher's method as well as vibrational circular dichroism (VCD) spectra calculation and it was determined as 6R, 7R, 9R, and 10R. It was found that compound 1 was not cytotoxic to human cancer cells, including A549 (lung cancer), AGS (gastric cancer), and Caco-2 (colorectal cancer) below the concentration of 25 μM. However, compound 1 was shown to significantly decrease cancer-cell migration and invasion and soft-agar colony-formation ability at concentrations ranging from 1 to 5 μM by downregulating the expression level of KITENIN and upregulating the expression level of KAI1. Compound 1 suppressed β-catenin-mediated TOPFLASH activity and its downstream targets in AGS, A549, and Caco-2 and slightly suppressed the Notch signal pathway in three cancer cells. Furthermore, 1 also reduced the number of metastatic nodules in an intraperitoneal xenograft mouse model.

Influenza is one of the leading causes of disease-related mortalities worldwide. Several strategies have been implemented during the past decades to hinder the replication cycle of influenza viruses, all of which have resulted in the emergence of resistant virus strains. The most recent example is baloxavir marboxil, where a single mutation in the active site of the target endonuclease domain of the RNA-dependent-RNA polymerase renders the recent FDA approved compound ∼1000-fold less effective. Raltegravir is a first-in-class HIV inhibitor that shows modest activity to the endonuclease. Here, we have used structure-guided approaches to create rationally designed derivative molecules that efficiently engage the endonuclease active site. The design strategy was driven by our previously published structures of endonuclease-substrate complexes, which allowed us to target functionally conserved residues and reduce the likelihood of resistance mutations. We succeeded in developing low nanomolar equipotent inhibitors of both wild-type and baloxavir-resistant endonuclease. We also developed macrocyclic versions of these inhibitors that engage the active site in the same manner as their 'open' counterparts but with reduced affinity. Structural analyses provide clear avenues for how to increase the affinity of these cyclic compounds.

Previous chemical investigation of the Irish deep-sea soft coral Duva florida led to the identification of tuaimenal A (10), a new merosesquiterpene containing a highly substituted chromene core and modest cytotoxicity against cervical cancer. Further MS/MS and NMR-guided investigation of this octocoral has resulted in the isolation and characterization of seven additional tuaimenal analogs, B-H (1-7), as well as two known A-ring aromatized steroids (8, 9), and additional tuaimenal A (10). Tuaimenals B, F, and G (1, 5, 6), bearing an oxygen at the C5 position, as well as monocyclic tuaimenal H (7), show increased cervical cancer inhibition profiles in comparison to that of 10. Tuaimenal G further displayed potent, selective cytotoxicity with an EC50 value of 0.04 μM against the C33A cell line compared to the CaSki cell line (EC50 20 μM). These data reveal the anticancer properties of tuaimenal analogs and suggest unique antiproliferation mechanisms across these secondary metabolites.

Microtubule (MT)-stabilizing 1,2,4-triazolo[1,5-a]pyrimidines (TPDs) hold promise as candidate therapeutics for Alzheimer's disease (AD) and other neurodegenerative conditions. However, depending on the choice of substituents around the TPD core, these compounds can elicit markedly different cellular phenotypes that likely arise from the interaction of TPD congeners with either one or two spatially distinct binding sites within tubulin heterodimers (i.e., the seventh site and the vinca site). In the present study, we report the design, synthesis, and evaluation of a series of new TPD congeners, as well as matched molecular pair analyses and computational studies, that further elucidate the structure-activity relationships of MT-active TPDs. These studies led to the identification of novel MT-normalizing TPD candidates that exhibit favorable ADME-PK, including brain penetration and oral bioavailability, as well as brain pharmacodynamic activity.

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

As one of the first families of marine natural products to undergo clinical trials, the didemnin depsipeptides have played a significant role in inspiring the discovery of marine drugs. Originally developed as anticancer therapeutics, the recent re-evaluation of these compounds including synthetically derived dehydrodidemnin B or Aplidine, has led to their advancement towards antiviral applications. While conventionally associated with production in colonial tunicates of the family Didemnidae, recent studies have identified their biosynthetic gene clusters from the marine-derived bacteria Tistrella mobilis. While these studies confirm the production of didemnin X/Y, the low titer and general lack of understanding of their biosynthesis in Tistrella currently prevents the development of effective microbial or synthetic biological approaches for their production. To this end, we conducted a survey of known species of Tistrella and report on their ability to produce the didemnin depsipeptides. These data were used to develop conditions to produce didemnin B at titers over 15 mg/L.

ARS-interacting multifunctional proteins 2 (AIMP2) is known to be a powerful tumour suppressor. However, the target AIMP2-DX2, AIMP2-lacking exon 2, is often detected in many cancer patients and cells. The predominant approach for targeting AIMP-DX2 has been attempted via small molecule mediated inhibition, but due to the lack of satisfactory activity against AIMP2-DX2, new therapeutic strategies are needed to develop a novel drug for AIMP2-DX2. Here, we report the use of the PROTAC strategy that combines small-molecule AIMP2-DX2 inhibitors with selective E3-ligase ligands with optimised linkers. Consequently, candidate compound 45 was found to be a degrader of AIMP2-DX2. Together, these findings demonstrate that our PROTAC technology targeting AIMP2-DX2 would be a potential new strategy for future lung cancer treatment.

Black band disease is a globally distributed and easily recognizable coral disease. Despite years of study, the etiology of this coral disease, which impacts dozens of stony coral species, is not completely understood. Although black band disease mats are predominantly composed of the cyanobacterial species Roseofilum reptotaenium, other filamentous cyanobacterial strains and bacterial heterotrophs are readily detected. Through chemical ecology and metagenomic sequencing, we uncovered cryptic strains of Roseofilum species from Siderastrea siderea corals that differ from those on other corals in the Caribbean and Pacific. Isolation of metabolites from Siderastrea-derived Roseofilum revealed the prevalence of unique forms of looekeyolides, distinct from previously characterized Roseofilum reptotaenium strains. In addition, comparative genomics of Roseofilum strains showed that only Siderastrea-based Roseofilum strains have the genetic capacity to produce lasso peptides, a family of compounds with diverse biological activity. All nine Roseofilum strains examined here shared the genetic capacity to produce looekeyolides and malyngamides, suggesting these compounds support the ecology of this genus. Similar biosynthetic gene clusters are not found in other cyanobacterial genera associated with black band disease, which may suggest that looekeyolides and malyngamides contribute to disease etiology through yet unknown mechanisms.

New series of thiazolyl-pyrazoline derivatives (7a-7d, 10a-10d and 13a-13f) have been synthesised and assessed for their potential EGFR and VEGFR-2 inhibitory activities. Compounds 10b and 10d exerted potent and selective inhibitory activity towards the two receptor tyrosine kinases; EGFR (IC50 = 40.7 ± 1.0 and 32.5 ± 2.2 nM, respectively) and VEGFR-2 (IC50 = 78.4 ± 1.5 and 43.0 ± 2.4 nM, respectively). The best anti-proliferative activity for the examined thiazolyl-pyrazolines was observed against the non-small lung cancer cells (NSCLC). Compounds 10b and 10d displayed pronounced efficacy against A549 (IC50 = 4.2 and 2.9 µM, respectively) and H441 cell lines (IC50 = 4.8 and 3.8 µM, respectively). Moreover, our results indicated that 10b and 10d were much more effective towards EGFR-mutated NSCLC cell lines (NCI-H1650 and NCI-H1975 cells) than gefitinib. Finally, compounds 10b and 10d induce G2/M cell cycle arrest and apoptosis and inhibit migration in A549 cancerous cells.

In this study, different assortments of 2-arylquinolines and 2,6-diarylquinolines have been developed. Recently, we have developed a new series of 6,7-dimethoxy-4-alkoxy-2-arylquinolines as Topoisomerase I (TOP1) inhibitors with potent anticancer activity. Utilising the SAR outputs from this study, we tried to enhance anticancer and TOP1 inhibitory activities. Though target quinolines demonstrated potent antiproliferative effect, specifically against colorectal cancer DLD-1 and HCT-116, they showed weak TOP1 inhibition which may be attributable to their non-coplanarity. Thereafter, screening against kinase panel revealed their dual inhibitory activity against EGFR and FAK. Quinolines 6f, 6h, 6i, and 20f were the most potent EGFR inhibitors (IC50s = 25.39, 20.15, 22.36, and 24.81 nM, respectively). Meanwhile, quinolines 6f, 6h, 6i, 16d, and 20f exerted the best FAK inhibition (IC50s = 22.68, 14.25, 18.36, 17.36, and 15.36 nM, respectively). Finally, molecular modelling was employed to justify the promising EGFR/FAK inhibition. The study outcomes afforded the first reported quinolines with potent EGFR/FAK dual inhibition.

A series of novel 1,2,3-triazole-linked ciprofloxacin-chalcones 4a-j were synthesised as potential anticancer agents. Hybrids 4a-j exhibited remarkable anti-proliferative activity against colon cancer cells. Compounds 4a-j displayed IC50s ranged from 2.53-8.67 µM, 8.67-62.47 µM, and 4.19-24.37 µM for HCT116, HT29, and Caco-2 cells; respectively, whereas the doxorubicin, showed IC50 values of 1.22, 0.88, and 4.15 µM. Compounds 4a, 4b, 4e, 4i, and 4j were the most potent against HCT116 with IC50 values of 3.57, 4.81, 4.32, 4.87, and 2.53 µM, respectively, compared to doxorubicin (IC50 = 1.22 µM). Also, hybrids 4a, 4b, 4e, 4i, and 4j exhibited remarkable inhibitory activities against topoisomerase I, II, and tubulin polymerisation. They increased the protein expression level of γH2AX, indicating DNA damage, and arrested HCT116 in G2/M phase, possibly through the ATR/CHK1/Cdc25C pathway. Thus, the novel ciprofloxacin hybrids could be exploited as potential leads for further investigation as novel anticancer medicines to fight colorectal carcinoma.

The worldwide COVID-19 pandemic caused by the coronavirus SARS-CoV-2 urgently demands novel direct antiviral treatments. The main protease (Mpro) and papain-like protease (PLpro) are attractive drug targets among coronaviruses due to their essential role in processing the polyproteins translated from the viral RNA. In this study, we virtually screened 688 naphthoquinoidal compounds and derivatives against Mpro of SARS-CoV-2. Twenty-four derivatives were selected and evaluated in biochemical assays against Mpro using a novel fluorogenic substrate. In parallel, these compounds were also assayed with SARS-CoV-2 PLpro. Four compounds inhibited Mpro with half-maximal inhibitory concentration (IC50) values between 0.41 μM and 9.0 μM. In addition, three compounds inhibited PLpro with IC50 ranging from 1.9 μM to 3.3 μM. To verify the specificity of Mpro and PLpro inhibitors, our experiments included an assessment of common causes of false positives such as aggregation, high compound fluorescence, and inhibition by enzyme oxidation. Altogether, we confirmed novel classes of specific Mpro and PLpro inhibitors. Molecular dynamics simulations suggest stable binding modes for Mpro inhibitors with frequent interactions with residues in the S1 and S2 pockets of the active site. For two PLpro inhibitors, interactions occur in the S3 and S4 pockets. In summary, our structure-based computational and biochemical approach identified novel naphthoquinonal scaffolds that can be further explored as SARS-CoV-2 antivirals.

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).

The soluble epoxide hydrolase (sEH) has been suggested as a pharmacological target for the treatment of several diseases, including pain-related disorders. Herein, we report further medicinal chemistry around new benzohomoadamantane-based sEH inhibitors (sEHI) in order to improve the drug metabolism and pharmacokinetics properties of a previous hit. After an extensive in vitro screening cascade, molecular modeling, and in vivo pharmacokinetics studies, two candidates were evaluated in vivo in a murine model of capsaicin-induced allodynia. The two compounds showed an anti-allodynic effect in a dose-dependent manner. Moreover, the most potent compound presented robust analgesic efficacy in the cyclophosphamide-induced murine model of cystitis, a well-established model of visceral pain. Overall, these results suggest painful bladder syndrome as a new possible indication for sEHI, opening a new range of applications for them in the visceral pain field.

LIMKs are important regulators of actin and microtubule dynamics, and they play essential roles in many cellular processes. Deregulation of LIMKs has been linked to the development of diverse diseases, including cancers and cognitive disabilities, but well-characterized inhibitors known as chemical probes are still lacking. Here, we report the characterization of three highly selective LIMK1/2 inhibitors covering all canonical binding modes (type I/II/III) and the structure-based design of the type II/III inhibitors. Characterization of these chemical probes revealed a low nanomolar affinity for LIMK1/2, and all inhibitors 1 (LIMKi3; type I), 48 (TH470; type II), and 15 (TH257; type III) showed excellent selectivity in a comprehensive scanMAX kinase selectivity panel. Phosphoproteomics revealed remarkable differences between type I and type II inhibitors compared with the allosteric inhibitor 15. In phenotypic assays such as neurite outgrowth models of fragile X-chromosome, 15 showed promising activity, suggesting the potential application of allosteric LIMK inhibitors treating this orphan disease.

The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host-guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host-guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.