Identification of potent pan-ephrin receptor kinase inhibitors using DNA-encoded chemistry technology.

EPH receptors (EPHs), the largest family of tyrosine kinases, phosphorylate downstream substrates upon binding of ephrin cell surface-associated ligands. In a large cohort of endometriotic lesions from individuals with endometriosis, we found that EPHA2 and EPHA4 expressions are increased in endometriotic lesions relative to normal eutopic endometrium. Because signaling through EPHs is associated with increased cell migration and invasion, we hypothesized that chemical inhibition of EPHA2/4 could have therapeutic value. We screened DNA-encoded chemical libraries (DECL) to rapidly identify EPHA2/4 kinase inhibitors. Hit compound, CDD-2693, exhibited picomolar/nanomolar kinase activity against EPHA2 (Ki: 4.0 nM) and EPHA4 (Ki: 0.81 nM). Kinome profiling revealed that CDD-2693 bound to most EPH family and SRC family kinases. Using NanoBRET target engagement assays, CDD-2693 had nanomolar activity versus EPHA2 (IC50: 461 nM) and EPHA4 (IC50: 40 nM) but was a micromolar inhibitor of SRC, YES, and FGR. Chemical optimization produced CDD-3167, having picomolar biochemical activity toward EPHA2 (Ki: 0.13 nM) and EPHA4 (Ki: 0.38 nM) with excellent cell-based potency EPHA2 (IC50: 8.0 nM) and EPHA4 (IC50: 2.3 nM). Moreover, CDD-3167 maintained superior off-target cellular selectivity. In 12Z endometriotic epithelial cells, CDD-2693 and CDD-3167 significantly decreased EFNA5 (ligand) induced phosphorylation of EPHA2/4, decreased 12Z cell viability, and decreased IL-1ß-mediated expression of prostaglandin synthase 2 (PTGS2). CDD-2693 and CDD-3167 decreased expansion of primary endometrial epithelial organoids from patients with endometriosis and decreased Ewing's sarcoma viability. Thus, using DECL, we identified potent pan-EPH inhibitors that show specificity and activity in cellular models of endometriosis and cancer.

Perspectives and challenges in developing small molecules targeting purine nucleoside phosphorylase.

As a cytosolic enzyme involved in the purine salvage pathway metabolism, purine nucleoside phosphorylase (PNP) plays an important role in a variety of cellular functions but also in immune system, including cell growth, apoptosis and cancer development and progression. Based on its T-cell targeting profile, PNP is a potential target for the treatment of some malignant T-cell proliferative cancers including lymphoma and leukemia, and some specific immunological diseases. Numerous small-molecule PNP inhibitors have been developed so far. However, only Peldesine, Forodesine and Ulodesine have entered clinical trials and exhibited some potential for the treatment of T-cell leukemia and gout. The most recent direction in PNP inhibitor development has been focused on PNP small-molecule inhibitors with better potency, selectivity, and pharmacokinetic property. In this perspective, considering the structure, biological functions, and disease relevance of PNP, we highlight the recent research progress in PNP small-molecule inhibitor development and discuss prospective strategies for designing additional PNP therapeutic agents.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro.

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.

Hybrid Small-Molecule/Protein Fluorescent Probes.

Hybrid small-molecule/protein fluorescent probes are powerful tools for visualizing protein localization and function in living cells. These hybrid probes are constructed by diverse site-specific chemical protein labeling approaches through chemical reactions to exogenous peptide/small protein tags, enzymatic post-translational modifications, bioorthogonal reactions for genetically incorporated unnatural amino acids, and ligand-directed chemical reactions. The hybrid small-molecule/protein fluorescent probes are employed for imaging protein trafficking, conformational changes, and bioanalytes surrounding proteins. In addition, fluorescent hybrid probes facilitate visualization of protein dynamics at the single-molecule level and the defined structure with super-resolution imaging. In this review, we discuss development and the bioimaging applications of fluorescent probes based on small-molecule/protein hybrids.

Lead compound profiling for small molecule inhibitors of the REV1-CT/RIR Translesion synthesis Protein-Protein interaction.

Translesion synthesis (TLS) is a cellular mechanism through which actively replicating cells recruit specialized, low-fidelity DNA polymerases to damaged DNA to allow for replication past these lesions. REV1 is one of these TLS DNA polymerases that functions primarily as a scaffolding protein to organize the TLS heteroprotein complex and ensure replication occurs in the presence of DNA lesions. The C-Terminal domain of REV1 (REV1-CT) forms many protein-protein interactions (PPIs) with other TLS polymerases, making it essential for TLS function and a promising drug target for anti-cancer drug development. We utilized several lead identification strategies to identify various small molecules capable of disrupting the PPI between REV1-CT and the REV1 Interacting Regions (RIR) present in several other TLS polymerases. These lead compounds were profiled in several in vitro potency and PK assays to identify two scaffolds (1 and 6) as the most promising for further development. Both 1 and 6 synergized with cisplatin in a REV1-dependent fashion and demonstrated promising in vivo PK and toxicity profiles.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Identification and characterization of small molecule inhibitors of the LINE-1 retrotransposon endonuclease.

The long interspersed nuclear element-1 (LINE-1 or L1) retrotransposon is the only active autonomously replicating retrotransposon in the human genome. L1 harms the cell by inserting new copies, generating DNA damage, and triggering inflammation. Therefore, L1 inhibition could be used to treat many diseases associated with these processes. Previous research has focused on inhibition of the L1 reverse transcriptase due to the prevalence of well-characterized inhibitors of related viral enzymes. Here we present the L1 endonuclease as another target for reducing L1 activity. We characterize structurally diverse small molecule endonuclease inhibitors using computational, biochemical, and biophysical methods. We also show that these inhibitors reduce L1 retrotransposition, L1-induced DNA damage, and inflammation reinforced by L1 in senescent cells. These inhibitors could be used for further pharmacological development and as tools to better understand the life cycle of this element and its impact on disease processes.

Innovative, combinatorial and high-throughput approaches to degrader synthesis.

Targeted protein degraders such as PROTACs and molecular glues are a rapidly emerging therapeutic modality within industry and academia. Degraders possess unique mechanisms of action that lead to the removal of specific proteins by co-opting the cell's natural degradation mechanisms via induced proximity. Their optimisation thus far has often been largely empirical, requiring the synthesis and screening of a large number of analogues. In addition, the synthesis and development of degraders is often challenging, leading to lengthy optimisation campaigns to deliver candidate-quality compounds. This review highlights how the synthesis of degraders has evolved in recent years, in particular focusing on means of applying high-throughput chemistry and screening approaches to expedite these timelines, which we anticipate to be valuable in shaping the future of degrader optimisation campaigns.

The novel small molecule E0924G dually regulates bone formation and bone resorption through activating the PPARδ signaling pathway to prevent bone loss in ovariectomized rats and senile mice.

Osteoporosis is particularly prevalent among postmenopausal women and the elderly. In the present study, we investigated the effect of the novel small molecule E0924G (N-(4-methoxy-pyridine-2-yl)-5-methylfuran-2-formamide) on osteoporosis. E0924G significantly increased the protein expression levels of osteoprotegerin (OPG) and runt-related transcription factor 2 (RUNX2), and thus significantly promoted osteogenesis in MC3T3-E1 cells. E0924G also significantly decreased osteoclast differentiation and inhibited bone resorption and F-actin ring formation in receptor activator of NF-κB ligand (RANKL)-induced osteoclasts from RAW264.7 macrophages. Importantly, oral administration of E0924G in both ovariectomized (OVX) rats and SAMP6 senile mice significantly increased bone mineral density and decreased bone loss compared to OVX controls or SAMR1 mice. Further mechanistic studies showed that E0924G could bind to and then activate peroxisome proliferator-activated receptor delta (PPARδ), and the pro-osteoblast effect and the inhibition of osteoclast differentiation induced by E0924G were significantly abolished when PPARδ was knocked down or inhibited. In conclusion, these data strongly suggest that E0924G has the potential to prevent OVX-induced and age-related osteoporosis by dual regulation of bone formation and bone resorption through activation of the PPARδ signaling pathway.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

DeepP450: Predicting Human P450 Activities of Small Molecules by Integrating Pretrained Protein Language Model and Molecular Representation.

Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of CYP activity and substrate identification essential for therapeutic development. Here, we established a deep learning model for assessing potential CYP substrates, DeepP450, by fine-tuning protein and molecule pretrained models through feature integration with cross-attention and self-attention layers. This model exhibited high prediction accuracy (0.92) on the test set, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.89 to 0.98 in substrate/nonsubstrate predictions across the nine major human CYPs, surpassing current benchmarks for CYP activity prediction. Notably, DeepP450 uses only one model to predict substrates/nonsubstrates for any of the nine CYPs and exhibits certain generalizability on novel compounds and different categories of human CYPs, which could greatly facilitate early stage drug design by avoiding CYP-reactive compounds.

Visualized Tracking and Multidimensional Assessing of Mitochondria-Associated Pyroptosis in Cancer Cells by a Small-Molecule Fluorescent Probe.

Pyroptosis is closely related to the development and treatment of various cancers; thus, comprehensive studies of the correlations between pyroptosis and its inductive or inhibitive factors can provide new ideas for the intervention and diagnosis of tumors. The dysfunction of mitochondria may induce pyroptosis in cancer cells, which can be reflected by the fluctuations of the microenvironmental parameters in mitochondria as well as the changes of mitochondrial DNA level and morphology, etc. To precisely track and assess the mitochondria-associated pyroptosis process, simultaneous visualization of changes in multiphysiological parameters in mitochondria is highly desirable. In this work, we reported a nonreaction-based, multifunctional small-molecule fluorescent probe Mito-DK with the capability of crosstalk-free response to polarity and mtDNA as well as mitochondrial morphology. Accurate assessment of mitochondria-associated pyroptosis induced by palmitic acid/H2O2 was achieved through monitoring changes in mitochondrial multiple parameters with the help of Mito-DK. In particular, the pyroptosis-inducing ability of an antibiotic doxorubicin and the pyroptosis-inhibiting capacity of an anticancer agent puerarin were evaluated by Mito-DK. These results provide new perspectives for visualizing mitochondria-associated pyroptosis and offer new approaches for screening pyroptosis-related anticancer agents.

Discovery of Small Molecule Interleukin 17A Inhibitors with Novel Binding Mode and Stoichiometry: Optimization of DNA-Encoded Chemical Library Hits to In Vivo Active Compounds.

Dysregulation of IL17A drives numerous inflammatory and autoimmune disorders with inhibition of IL17A using antibodies proven as an effective treatment. Oral anti-IL17 therapies are an attractive alternative option, and several preclinical small molecule IL17 inhibitors have previously been described. Herein, we report the discovery of a novel class of small molecule IL17A inhibitors, identified via a DNA-encoded chemical library screen, and their subsequent optimization to provide in vivo efficacious inhibitors. These new protein-protein interaction (PPI) inhibitors bind in a previously undescribed mode in the IL17A protein with two copies binding symmetrically to the central cavities of the IL17A homodimer.

Mitochondria-Selective Dicationic Small-Molecule Ligand Targeting G-Quadruplex Structures for Human Colorectal Cancer Therapy.

Mitochondria are important drug targets for anticancer and other disease therapies. Certain human mitochondrial DNA sequences capable of forming G-quadruplex structures (G4s) are emerging drug targets of small molecules. Despite some mitochondria-selective ligands being reported for drug delivery against cancers, the ligand design is mostly limited to the triphenylphosphonium scaffold. The ligand designed with lipophilic small-sized scaffolds bearing multipositive charges targeting the unique feature of high mitochondrial membrane potential (MMP) is lacking and most mitochondria-selective ligands are not G4-targeting. Herein, we report a new small-sized dicationic lipophilic ligand to target MMP and mitochondrial DNA G4s to enhance drug delivery for anticancer. The ligand showed marked alteration of mitochondrial gene expression and substantial induction of ROS production, mitochondrial dysfunction, DNA damage, cellular senescence, and apoptosis. The ligand also exhibited high anticancer activity against HCT116 cancer cells (IC50, 3.4 µM) and high antitumor efficacy in the HCT116 tumor xenograft mouse model (∼70% tumor weight reduction).

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

Small-molecule targeting PKM2 provides a molecular basis of lactylation-dependent fibroblast-like synoviocytes proliferation inhibition against rheumatoid arthritis.

Fibroblast-like synoviocytes (FLS) play an important role in rheumatoid arthritis (RA)-related swelling and bone damage. Therefore, novel targets for RA therapy in FLS are urgently discovered for improving pathologic phenomenon, especially joint damage and dyskinesia. Here, we suggested that pyruvate kinase M2 (PKM2) in FLS represented a pharmacological target for RA treatment by antimalarial drug artemisinin (ART). We demonstrated that ART selectively inhibited human RA-FLS and rat collagen-induced arthritis (CIA)-FLS proliferation and migration without observed toxic effects. In particular, the identification of targets revealed that PKM2 played a crucial role as a primary regulator of the cell cycle, leading to the heightened proliferation of RA-FLS. ART exhibited a direct interaction with PKM2, resulting in an allosteric modulation that enhances the lactylation modification of PKM2. This interaction further promoted the binding of p300, ultimately preventing the nuclear translocation of PKM2 and inducing cell cycle arrest at the S phase. In vivo, ART obviously suppressed RA-mediated synovial hyperplasia, bone damage and inflammatory response to further improve motor behavior in CIA-rats. Taken together, these findings indicate that directing interventions towards PKM2 in FLS could offer a hopeful avenue for pharmaceutical treatments of RA through the regulation of cell cycle via PKM2 lactylation.

Identification of a novel histone H2A mono-ubiquitination-inhibiting cell-active small molecule.

Histone H2A mono-ubiquitination plays important roles in epigenetic gene expression and is also involved in tumorigenesis. Small molecules controlling H2A ubiquitination are of interest as potential chemical tools and anticancer drugs. To identify novel small molecule inhibitors of H2A ubiquitination, we synthesized and evaluated several compounds designed based on PRT4165 (1), which is a reported histone ubiquitin ligase RING1A inhibitor. We found that compound 11b strongly inhibited the viability and reduced histone H2A mono-ubiquitination in human osteosarcoma U2OS cells. Therefore, compound 11b is a promising lead compound for the development of H2A histone ubiquitination-inhibiting small molecules.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

Identification of Leishmania donovani PEX5-PTS1 Interaction Inhibitors through Fluorescence Polarization-Based High-Throughput Screening.

Leishmaniasis, an infectious disease caused by pathogenic Leishmania parasites, affects millions of people in developing countries, and its re-emergence in developed countries, particularly in Europe, poses a growing public health concern. The limitations of current treatments and the absence of effective vaccines necessitate the development of novel therapeutics. In this study, we focused on identifying small molecule inhibitors which prevents the interaction between peroxin 5 (PEX5) and peroxisomal targeting signal 1 (PTS1), pivotal for kinetoplastid parasite survival. The Leishmania donovani PEX5, containing a C-terminal tetratricopeptide repeat (TPR) domain, was expressed and purified, followed by the quantification of kinetic parameters of PEX5-PTS1 interactions. A fluorescence polarization-based high-throughput screening assay was developed and small molecules inhibiting the LdPEX5-PTS1 interaction were discovered through the screening of a library of 51,406 compounds. Based on the confirmatory assay, nine compounds showed half maximal inhibitory concentration (IC50) values ranging from 3.89 to 24.50 µM. In silico docking using a homology model of LdPEX5 elucidated that the molecular interactions between LdPEX5 and the inhibitors share amino acids critical for PTS1 binding. Notably, compound P20 showed potent activity against the growth of L. donovani promastigotes, L. major promastigotes, and Trypanosoma brucei blood stream form, with IC50 values of 12.16, 19.21, and 3.06 µM, respectively. The findings underscore the potential of targeting LdPEX5-PTS1 interactions with small molecule inhibitors as a promising strategy for the discovery of new anti-parasitic compounds.