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.

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-.

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.

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.

dMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

The small molecule CBR-5884 inhibits the Candida albicans phosphatidylserine synthase.

Systemic infections by Candida spp. are associated with high mortality rates, partly due to limitations in current antifungals, highlighting the need for novel drugs and drug targets. The fungal phosphatidylserine synthase, Cho1, from Candida albicans is a logical antifungal drug target due to its importance in virulence, absence in the host, and conservation among fungal pathogens. Inhibitors of Cho1 could serve as lead compounds for drug development, so we developed a target-based screen for inhibitors of purified Cho1. This enzyme condenses serine and cytidyldiphosphate-diacylglycerol (CDP-DAG) into phosphatidylserine (PS) and releases cytidylmonophosphate (CMP). Accordingly, we developed an in vitro nucleotidase-coupled malachite-green-based high throughput assay for purified C. albicans Cho1 that monitors CMP production as a proxy for PS synthesis. Over 7,300 molecules curated from repurposing chemical libraries were interrogated in primary and dose-responsivity assays using this platform. The screen had a promising average Z' score of ~0.8, and seven compounds were identified that inhibit Cho1. Three of these, ebselen, LOC14, and CBR-5884, exhibited antifungal effects against C. albicans cells, with fungicidal inhibition by ebselen and fungistatic inhibition by LOC14 and CBR-5884. Only CBR-5884 showed evidence of disrupting in vivo Cho1 function by inducing phenotypes consistent with the cho1∆∆ mutant, including a reduction of cellular PS levels. Kinetics curves and computational docking indicate that CBR-5884 competes with serine for binding to Cho1 with a Ki of 1,550 ± 245.6 nM. Thus, this compound has the potential for development into an antifungal compound. IMPORTANCE: Fungal phosphatidylserine synthase (Cho1) is a logical antifungal target due to its crucial role in the virulence and viability of various fungal pathogens, and since it is absent in humans, drugs targeted at Cho1 are less likely to cause toxicity in patients. Using fungal Cho1 as a model, there have been two unsuccessful attempts to discover inhibitors for Cho1 homologs in whole-cell screens prior to this study. The compounds identified in these attempts do not act directly on the protein, resulting in the absence of known Cho1 inhibitors. The significance of our research is that we developed a high-throughput target-based assay and identified the first Cho1 inhibitor, CBR-5884, which acts both on the purified protein and its function in the cell. This molecule acts as a competitive inhibitor with a Ki value of 1,550 ± 245.6 nM and, thus, has the potential for development into a new class of antifungals targeting PS synthase.

Expanding the ligand spaces for E3 ligases for the design of protein degraders.

Targeted protein degradation (TPD) has recently emerged as an exciting new drug modality. However, the strategy of developing small molecule-based protein degraders has evolved over the past two decades and has now established molecular tags that are already in clinical use, as well as chimeric molecules, PROteolysis TArgeting Chimeras (PROTACs), based mainly on ligand systems developed for the two E3 ligases CRBN and VHL. The large size of the human E3 ligase family suggests that PROTACs can be developed by targeting a large diversity of E3 ligases, some of which have restricted expression patterns with the potential to design disease- or tissue-specific degraders. Indeed, many new E3 ligands have been published recently, confirming the druggability of E3 ligases. This review summarises recent data on E3 ligases and highlights the challenges in developing these molecules into efficient PROTACs rivalling the established degrader systems.

Allosteric Modulation of the YAP/TAZ-TEAD Interaction by Palmitoylation and Small-Molecule Inhibitors.

The Hippo signaling pathway is a highly conserved signaling network that plays a central role in regulating cellular growth, proliferation, and organ size. This pathway consists of a kinase cascade that integrates various upstream signals to control the activation or inactivation of YAP/TAZ proteins. Phosphorylated YAP/TAZ is sequestered in the cytoplasm; however, when the Hippo pathway is deactivated, it translocates into the nucleus, where it associates with TEAD transcription factors. This partnership is instrumental in regulating the transcription of progrowth and antiapoptotic genes. Thus, in many cancers, aberrantly hyperactivated YAP/TAZ promotes oncogenesis by contributing to cancer cell proliferation, metastasis, and therapy resistance. Because YAP and TAZ exert their oncogenic effects by binding with TEAD, it is critical to understand this key interaction to develop cancer therapeutics. Previous research has indicated that TEAD undergoes autopalmitoylation at a conserved cysteine, and small molecules that inhibit TEAD palmitoylation disrupt effective YAP/TAZ binding. However, how exactly palmitoylation contributes to YAP/TAZ-TEAD interactions and how the TEAD palmitoylation inhibitors disrupt this interaction remains unknown. Utilizing molecular dynamics simulations, our investigation not only provides detailed atomistic insight into the YAP/TAZ-TEAD dynamics but also unveils that the inhibitor studied influences the binding of YAP and TAZ to TEAD in distinct manners. This discovery has significant implications for the design and deployment of future molecular interventions targeting this interaction.

AI is a viable alternative to high throughput screening: a 318-target study.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Inhibitors of the Thioesterase Activity of Mycobacterium tuberculosis Pks13 Discovered Using DNA-Encoded Chemical Library Screening.

DNA-encoded chemical library (DEL) technology provides a time- and cost-efficient method to simultaneously screen billions of compounds for their affinity to a protein target of interest. Here we report its use to identify a novel chemical series of inhibitors of the thioesterase activity of polyketide synthase 13 (Pks13) from Mycobacterium tuberculosis (Mtb). We present three chemically distinct series of inhibitors along with their enzymatic and Mtb whole cell potency, the measure of on-target activity in cells, and the crystal structures of inhibitor-enzyme complexes illuminating their interactions with the active site of the enzyme. One of these inhibitors showed a favorable pharmacokinetic profile and demonstrated efficacy in an acute mouse model of tuberculosis (TB) infection. These findings and assay developments will aid in the advancement of TB drug discovery.

Opportunities and perspectives of small molecular phosphodiesterase inhibitors in neurodegenerative diseases.

Phosphodiesterase (PDE) is a superfamily of enzymes that are responsible for the hydrolysis of two second messengers: cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). PDE inhibition promotes the gene transcription by activating cAMP-response element binding protein (CREB), initiating gene transcription of brain-derived neurotrophic factor (BDNF). The procedure exerts neuroprotective profile, and motor and cognitive improving efficacy. From this point of view, PDE inhibition will provide a promising therapeutic strategy for treating neurodegenerative disorders. Herein, we summarized the PDE inhibitors that have entered the clinical trials or been discovered in recent five years. Well-designed clinical or preclinical investigations have confirmed the effectiveness of PDE inhibitors, such as decreasing Aß oligomerization and tau phosphorylation, alleviating neuro-inflammation and oxidative stress, modulating neuronal plasticity and improving long-term cognitive impairment.

Modulators for palmitoylation of proteins and small molecules.

As an essential form of lipid modification for maintaining vital cellular functions, palmitoylation plays an important role in in the regulation of various physiological processes, serving as a promising therapeutic target for diseases like cancer and neurological disorders. Ongoing research has revealed that palmitoylation can be categorized into three distinct types: N-palmitoylation, O-palmitoylation and S-palmitoylation. Herein this paper provides an overview of the regulatory enzymes involved in palmitoylation, including palmitoyltransferases and depalmitoylases, and discusses the currently available broad-spectrum and selective inhibitors for these enzymes.

Quantum chemistry calculation-aided discovery of potent small-molecule mimics of glutathione peroxidases for the treatment of cisplatin-induced hearing loss.

Hearing loss (HL) is a health burden that seriously affects the quality of life of cancer patients receiving platinum-based chemotherapy, and few FDA-approved treatment specifically targets this condition. The main mechanisms that contribute to cisplatin-induced hearing loss are oxidative stress and subsequent cell death, including ferroptosis revealed by us as a new mechanism recently. In this study, we employed the frontier molecular orbital (FMO) theory approach as a convenient prediction method for the glutathione peroxidase (GPx)-like activity of isoselenazolones and discovered new isoselenazolones with great GPx-like activity. Notably, compound 19 exhibited significant protective effects against cisplatin-induced hair cell (HC) damage in vitro and in vivo and effectively reverses cisplatin-induced hearing loss through oral administration. Further investigations revealed that this compound effectively alleviated hair cell oxidative stress, apoptosis and ferroptosis. This research highlights the potential of GPx mimics as a therapeutic strategy against cisplatin-induced hearing loss. The application of quantum chemistry (QC) calculations in the study of GPx mimics sheds light on the development of new, innovative treatments for hearing loss.

Privileged small molecules against neglected tropical diseases: A perspective from structure activity relationships.

Neglected tropical diseases (NTDs) comprise diverse infections with more incidence in tropical/sub-tropical areas. In spite of preventive and therapeutic achievements, NTDs are yet serious threats to the public health. Epidemiological reports of world health organization (WHO) indicate that more than 1.5 billion people are afflicted with at least one NTD type. Among NTDs, leishmaniasis, chagas disease (CD) and human African trypanosomiasis (HAT) result in substantial morbidity and death, particularly within impoverished countries. The statistical facts call for robust efforts to manage the NTDs. Currently, most of the anti-NTD drugs are engaged with drug resistance, lack of efficient vaccines, limited spectrum of pharmacological effect and adverse reactions. To circumvent the issue, numerous scientific efforts have been directed to the synthesis and pharmacological development of chemical compounds as anti-infectious agents. A survey of the anti-NTD agents reveals that the majority of them possess privileged nitrogen, sulfur and oxygen-based heterocyclic structures. In this review, recent achievements in anti-infective small molecules against parasitic NTDs are described, particularly from the SAR (Structure activity relationship) perspective. We also explore current advocating strategies to extend the scope of anti-NTD agents.

Research strategies of small molecules as chemotherapeutics to overcome multiple myeloma resistance.

Multiple myeloma (MM), a cancer of plasma cells, is the second most common hematological malignancy which is characterized by aberrant plasma cells infiltration in the bone marrow and complex heterogeneous cytogenetic abnormalities. Over the past two decades, novel treatment strategies such as proteasome inhibitors, immunomodulators, and monoclonal antibodies have significantly improved the relative survival rate of MM patients. However, the development of drug resistance results in the majority of MM patients suffering from relapse, limited treatment options and uncontrolled disease progression after relapse. There are urgent needs to develop and explore novel MM treatment strategies to overcome drug resistance and improve efficacy. Here, we review the recent small molecule therapeutic strategies for MM, and introduce potential new targets and corresponding modulators in detail. In addition, this paper also summarizes the progress of multi-target inhibitor therapy and protein degradation technology in the treatment of MM.

Discovery of the small molecular inhibitors against sclerostin loop3 as potential anti-osteoporosis agents by structural based virtual screening and molecular design.

Sclerostin is a secreted glycoprotein that expresses predominantly in osteocytes and inhibits bone formation by antagonizing the Wnt/ß-catenin signaling pathway, and the loop3 region of sclerostin has recently discovered as a novel therapeutic target for bone anabolic treatment without increasing cardiovascular risk. Herein, we used a structural based virtual screening to search for small molecular inhibitors selectively targeting sclerostin loop3. A novel natural product hit ZINC4228235 (THFA) was identified as the sclerostin loop3-selective inhibitor with a Kd value of 42.43 nM against sclerostin loop3. The simplification and derivation of THFA using molecular modeling-guided modification allowed the discovery of an effective and loop3-selective small molecular inhibitor, compound (4-(3-acetamidoprop-1-yn-1-yl)benzoyl)glycine (AACA), with improved binding affinity (Kd = 15.4 nM) compared to the hit THFA. Further in-vitro experiment revealed that compound AACA could attenuate the suppressive effect of transfected sclerostin on Wnt signaling and bone formation. These results make AACA as a potential candidate for development of anti-osteoporosis agents without increasing cardiovascular risk.

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/.

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.