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Wild-type P53-induced phosphatase 1 (WIP1), also known as

Nontypeable Haemophilus influenzae (NTHi) are clinically important Gram-negative bacteria that are responsible for various human mucosal diseases, including otitis media (OM). Recurrent OM caused by NTHi is common and infections which recur less than two weeks following antimicrobial therapy are largely attributable to recurrence of the same strain of bacteria. Toxin-antitoxin (TA) modules encoded by bacteria enable rapid responses to environmental stresses and are thought to facilitate growth arrest, persistence, and tolerance to antibiotics. The vapBC-1 locus of NTHi encodes a type II TA system, comprising the ribonuclease toxin VapC1 and its cognate antitoxin VapB1. The activity of VapC1 has been linked to the survival of NTHi during antibiotic treatment both in vivo and ex vivo. Therefore, inhibitors of VapC1 might serve as adjuvants to antibiotics, preventing NTHi from entering growth arrest and surviving; however, none have been reported to date. A truncated VapB1 peptide from a crystal structure of the VapBC-1 complex was used to generate pharmacophore queries to facilitate a scaffold hopping approach for the identification of small molecule VapC1 inhibitors. The National Center for Advancing Translational Sciences small molecule library was virtually screened using the shape-based method Rapid Overlay of Chemical Structures (ROCS) and the top-ranking hits were docked into the VapB1 binding pocket of VapC1. Two hundred virtual screening hits with the best docking scores were selected and tested in a biochemical VapC1 activity assay, which confirmed eight compounds as VapC1 inhibitors. An additional sixty compounds were selected with structural similarities to the confirmed VapC1 inhibitors, of which twenty inhibited VapC1 activity. Intracellular target engagement of five inhibitors was indicated by the destabilization of VapC1 within bacterial cells from a cellular thermal shift assay; however, no impact on bacterial growth was observed. Thus, this virtual screening and scaffold hopping approach enabled the discovery of VapC1 ribonuclease inhibitors that might serve as starting points for preclinical development.

Wildtype P53-induced phosphatase 1 (WIP1) is a member of the magnesium-dependent serine/threonine protein phosphatase (PPM) family and is induced by P53 in response to DNA damage. In several human cancers, the WIP1 protein is overexpressed, which is generally associated with a worse prognosis. Although WIP1 is an attractive therapeutic target, no potent, selective, and bioactive small-molecule modulator with favorable pharmacokinetics has been reported. Phosphatase enzymes are among the most challenging targets for small molecules because of the difficulty of achieving both modulator selectivity and bioavailability. Another major obstacle has been the availability of robust and physiologically relevant phosphatase assays that are suitable for high-throughput screening. Here, we describe orthogonal biochemical WIP1 activity assays that utilize phosphopeptides from native WIP1 substrates. We optimized an MS assay to quantify the enzymatically dephosphorylated peptide reaction product in a 384-well format. Additionally, a red-shifted fluorescence assay was optimized in a 1,536-well format to enable real-time WIP1 activity measurements through the detection of the orthogonal reaction product, inorganic phosphate. We validated these two optimized assays by quantitative high-throughput screening against the National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection and used secondary assays to confirm and evaluate inhibitors identified in the primary screen. Five inhibitors were further tested with an orthogonal WIP1 activity assay and surface plasmon resonance binding studies. Our results validate the application of miniaturized physiologically relevant and orthogonal WIP1 activity assays to discover small-molecule modulators from high-throughput screens.

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate {\mu} opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19., Comment: 27 pages, 6 figures

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.

Acquiring labeled data has been widely recognized as a major challenge in molecular property prediction. Since it generally requires a series of specialized biochemical experiments which are time-consuming, costly, as well as labor-intensive. The deficiency of labeled property data makes it difficult to learn a good prediction model. Here, we propose an RNN-based multi-label molecular property prediction method to alleviate the data scarcity issue in two stages: 1) utilize the abundant unlabeled SMILES data to pre-train a seq2seq model whose encoder learns to generate molecular fingerprint based on the given SMILES; and 2) finetune the pre-trained model on the labeled molecular property data. Since labeled data is limited, we train those properties with limited sample size jointly with other properties which contain relatively sufficient samples. This approach brings in the idea of multi-label training, which is able to pre-train and fine-tune the encoder network, as well as train the prediction network with a data augmentation strategy. Extensive experiments on molecular property prediction demonstrate that our proposed method has achieved superior performance compared with the state-of-the-art approaches on properties with limited sample size.

The spread of Plasmodium falciparum parasites resistant to most first-line antimalarials creates an imperative to enrich the drug discovery pipeline, preferably with curative compounds that can also act prophylactically. We report a phenotypic quantitative high-throughput screen (qHTS), based on concentration–response curves, which was designed to identify compounds active against Plasmodium liver and asexual blood stage parasites. Our qHTS screened over 450,000 compounds, tested across a range of 5 to 11 concentrations, for activity against Plasmodium falciparum asexual blood stages. Active compounds were then filtered for unique structures and drug-like properties and subsequently screened in a P. berghei liver stage assay to identify novel dual-active antiplasmodial chemotypes. Hits from thiadiazine and pyrimidine azepine chemotypes were subsequently prioritized for resistance selection studies, yielding distinct mutations in P. falciparum cytochrome b, a validated antimalarial drug target. The thiadiazine chemotype was subjected to an initial medicinal chemistry campaign, yielding a metabolically stable analog with sub-micromolar potency. Our qHTS methodology and resulting dataset provides a large-scale resource to investigate Plasmodium liver and asexual blood stage parasite biology and inform further research to develop novel chemotypes as causal prophylactic antimalarials.

Accumulation of α-synuclein is a main underlying pathological feature of Parkinson’s disease and α-synucleinopathies, for which lowering expression of the α-synuclein gene (SNCA) is a potential therapeutic avenue. Using a cell-based luciferase reporter of SNCA expression we performed a quantitative high-throughput screen of 155,885 compounds and identified A-443654, an inhibitor of the multiple functional kinase AKT, as a potent inhibitor of SNCA. HEK-293 cells with CAG repeat expanded ATXN2 (ATXN2-Q58 cells) have increased levels of α-synuclein. We found that A-443654 normalized levels of both SNCA mRNA and α-synuclein monomers and oligomers in ATXN2-Q58 cells. A-443654 also normalized levels of α-synuclein in fibroblasts and iPSC-derived dopaminergic neurons from a patient carrying a triplication of the SNCA gene. Analysis of autophagy and endoplasmic reticulum stress markers showed that A-443654 successfully prevented α-synuclein toxicity and restored cell function in ATXN2-Q58 cells, normalizing the levels of mTOR, LC3-II, p62, STAU1, BiP, and CHOP. A-443654 also decreased the expression of DCLK1, an inhibitor of α-synuclein lysosomal degradation. Our study identifies A-443654 and AKT inhibition as a potential strategy for reducing SNCA expression and treating Parkinson’s disease pathology.

GPR17 is a G-protein-coupled receptor (GPCR) implicated in the regulation of glucose metabolism and energy homeostasis. Such evidence is primarily drawn from mouse knockout studies and suggests GPR17 as a potential novel therapeutic target for the treatment of metabolic diseases. However, links between human GPR17 genetic variants, downstream cellular signaling, and metabolic diseases have yet to be reported. Here, we analyzed GPR17 coding sequences from control and disease cohorts consisting of individuals with adverse clinical metabolic deficits including severe insulin resistance, hypercholesterolemia, and obesity. We identified 18 nonsynonymous GPR17 variants, including eight variants that were exclusive to the disease cohort. We characterized the protein expression levels, membrane localization, and downstream signaling profiles of nine GPR17 variants (F43L, V96M, V103M, D105N, A131T, G136S, R248Q, R301H, and G354V). These nine GPR17 variants had similar protein expression and subcellular localization as wild-type GPR17; however, they showed diverse downstream signaling profiles. GPR17-G136S lost the capacity for agonist-mediated cAMP, Ca2+, and β-arrestin signaling. GPR17-V96M retained cAMP inhibition similar to GPR17-WT, but showed impaired Ca2+ and β-arrestin signaling. GPR17-D105N displayed impaired cAMP and Ca2+ signaling, but unaffected agonist-stimulated β-arrestin recruitment. The identification and functional profiling of naturally occurring human GPR17 variants from individuals with metabolic diseases revealed receptor variants with diverse signaling profiles, including differential signaling perturbations that resulted in GPCR signaling bias. Our findings provide a framework for structure–function relationship studies of GPR17 signaling and metabolic disease.

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in-silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5,435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4,071 compounds with quantitative data is able to predict the remaining 1,364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in-silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

With the rapid progress of AI in both academia and industry, Deep Learning has been widely introduced into various areas in drug discovery to accelerate its pace and cut R&D costs. Among all the problems in drug discovery, molecular property prediction has been one of the most important problems. Unlike general Deep Learning applications, the scale of labeled data is limited in molecular property prediction. To better solve this problem, Deep Learning methods have started focusing on how to utilize tremendous unlabeled data to improve the prediction performance on small-scale labeled data. In this paper, we propose a semi-supervised model named SMILES-BERT, which consists of attention mechanism based Transformer Layer. A large-scale unlabeled data has been used to pre-train the model through a Masked SMILES Recovery task. Then the pre-trained model could easily be generalized into different molecular property prediction tasks via fine-tuning. In the experiments, the proposed SMILES-BERT outperforms the state-of-the-art methods on all three datasets, showing the effectiveness of our unsupervised pre-training and great generalization capability of the pre-trained model.

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive models for kinetic solubility with in-house data generated from 11,780 compounds collected from over 200 NCATS intramural research projects. This represents one of the largest kinetic solubility datasets of high quality and integrity. Based on the customized atom type descriptors, the support vector classification (SVC) models were trained on 80% of the whole dataset, and exhibited high predictive performance for estimating the solubility of the remaining 20% compounds within the test set. The values of the area under the receiver operating characteristic curve (AUC-ROC) for the compounds in the test sets reached 0.93 and 0.91, when the threshold for insoluble compounds was set to 10 and 50 μg/mL respectively. The predictive models of aqueous solubility can be used to identify insoluble compounds in drug discovery pipeline, provide design ideas for improving solubility by analyzing the atom types associated with poor solubility and prioritize compound libraries to be purchased or synthesized.

Melanoma is an aggressive neoplasm with increasing incidence that is classified by the NCI as a recalcitrant cancer, i.e., a cancer with poor prognosis, lacking progress in diagnosis and treatment. In addition to conventional therapy, melanoma treatment is currently based on targeting the BRAF/MEK/ERK signaling pathway and immune checkpoints. As drug resistance remains a major obstacle to treatment success, advanced therapeutic approaches based on novel targets are still urgently needed. We reasoned that the base excision repair enzyme thymine DNA glycosylase (TDG) could be such a target for its dual role in safeguarding the genome and the epigenome, by performing the last of the multiple steps in DNA demethylation. Here we show that TDG knockdown in melanoma cell lines causes cell cycle arrest, senescence, and death by mitotic alterations; alters the transcriptome and methylome; and impairs xenograft tumor formation. Importantly, untransformed melanocytes are minimally affected by TDG knockdown, and adult mice with conditional knockout of Tdg are viable. Candidate TDG inhibitors, identified through a high-throughput fluorescence-based screen, reduced viability and clonogenic capacity of melanoma cell lines and increased cellular levels of 5-carboxylcytosine, the last intermediate in DNA demethylation, indicating successful on-target activity. These findings suggest that TDG may provide critical functions specific to cancer cells that make it a highly suitable anti-melanoma drug target. By potentially disrupting both DNA repair and the epigenetic state, targeting TDG may represent a completely new approach to melanoma therapy.

Graphical abstract, In response to the pandemic caused by SARS-CoV-2, we constructed a hybrid support vector machine (SVM) classification model using a set of publicly posted SARS-CoV-2 pseudotyped particle (PP) entry assay repurposing screen data to identify novel potent compounds as a starting point for drug development to treat COVID-19 patients. Two different molecular descriptor systems, atom typing descriptors and 3D fingerprints (FPs), were employed to construct the SVM classification models. Both models achieved reasonable performance, with the area under the curve of receiver operating characteristic (AUC-ROC) of 0.84 and 0.82, respectively. The consensus prediction outperformed the two individual models with significantly improved AUC-ROC of 0.91, where the compounds with inconsistent classifications were excluded. The consensus model was then used to screen the 173,898 compounds in the NCATS annotated and diverse chemical libraries. Of the 255 compounds selected for experimental confirmation, 116 compounds exhibited inhibitory activities in the SARS-CoV-2 PP entry assay with IC50 values ranged between 0.17 µM and 62.2 µM, representing an enrichment factor of 3.2. These 116 active compounds with diverse and novel structures could potentially serve as starting points for chemistry optimization for COVID-19 drug discovery.

Cytotoxicity is a critical property in determining the fate of a small molecule in the drug discovery pipeline. Cytotoxic compounds are identified and triaged in both target-based and cell-based phenotypic approaches due to their off-target toxicity or on-target and on-mechanism toxicity for oncology and neurodegenerative targets. It is critical that chemical-induced cytotoxicity be reliably predicted before drug candidates advance to the late stage of development, or more ideally, before compounds are synthesized. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in NCATS annotated libraries against four 'normal' cell lines (HEK 293, NIH 3T3, CRL-7250 and HaCat) using CellTiter-Glo (CTG) technology and constructed highly predictive models to estimate cytotoxicity from chemical structures. There are 5,241 non-redundant compounds having unambiguous activities in the four different cell lines, among which 11.8% compounds exhibited cytotoxicity in two or more cell lines and are thus labelled cytotoxic. The support vector classification (SVC) models trained with 80% randomly selected molecules achieved the area under the receiver operating characteristic curve (AUC-ROC) of 0.88 on average for the remaining 20% compounds in the test sets in 10 repeating experiments. Application of under-sampling rebalancing method further improved the averaged AUC-ROC to 0.90. Analysis of structural features shared by cytotoxic compounds may offer medicinal chemists heuristic design ideas to eliminate undesirable cytotoxicity. The profiling of cytotoxicity of drug-like molecules with annotated primary mechanism of action (MOA) will inform on the roles played by different targets or pathways in cellular viability. The predictive models for cytotoxicity (accessible at https://tripod.nih.gov/web_adme/cytotox.html) provide the scientific community a fast yet reliable way to prioritize molecules with little or no cytotoxicity for downstream development.

Drug repurposing approaches have the potential advantage of facilitating rapid and cost-effective development of new therapies. Particularly, the repurposing of drugs with known safety profiles in children could bypass or streamline toxicity studies. We employed a phenotypic screening paradigm on a panel of well-characterized cell lines derived from pediatric solid tumors against a collection of ∼3,800 compounds spanning approved drugs and investigational agents. Specifically, we employed titration-based screening where compounds were tested at multiple concentrations for their effect on cell viability. Molecular and cellular target enrichment analysis indicated that numerous agents across different therapeutic categories and modes of action had an antiproliferative effect, notably antiparasitic/protozoal drugs with non-classic antineoplastic activity. Focusing on active compounds with dosing and safety information in children according to the Children's Pharmacy Collaborative database, we identified compounds with therapeutic potential through further validation using 3D tumor spheroid models. Moreover, we show that antiparasitic agents induce cell death via apoptosis induction. This study demonstrates that our screening platform enables the identification of chemical agents with cytotoxic activity in pediatric cancer cell lines of which many have known safety/toxicity profiles in children. These agents constitute attractive candidates for efficacy studies in pre-clinical models of pediatric solid tumors.

Deregulation of ubiquitin conjugation or deconjugation has been implicated in the pathogenesis of many human diseases including cancer. The deubiquitinating enzyme USP1 (ubiquitin-specific protease 1), in association with UAF1 (USP1-associated factor 1), is a known regulator of DNA damage response and has been shown as a promising anticancer target. To further evaluate USP1/UAF1 as a therapeutic target, we conducted a quantitative high throughput screen of >400000 compounds and subsequent medicinal chemistry optimization of small molecules that inhibit the deubiquitinating activity of USP1/UAF1. Ultimately, these efforts led to the identification of ML323 (70) and related N-benzyl-2-phenylpyrimidin-4-amine derivatives, which possess nanomolar USP1/UAF1 inhibitory potency. Moreover, we demonstrate a strong correlation between compound IC50 values for USP1/UAF1 inhibition and activity in nonsmall cell lung cancer cells, specifically increased monoubiquitinated PCNA (Ub-PCNA) levels and decreased cell survival. Our results establish the druggability of the USP1/UAF1 deubiquitinase complex and its potential as a molecular target for anticancer therapies.

DYRK1B is a kinase over-expressed in certain cancer cells (including colon, ovarian, pancreatic, etc.). Recent publications have demonstrated inhibition of DYRK1B could be an attractive target for cancer therapy. From a data-mining effort, the team has discovered analogues of pyrido[2,3-d]pyrimidines as potent enantio-selective inhibitors of DYRK1B. Cells treated with a tool compound from this series showed the same cellular effects as down regulation of DYRK1B with siRNA. Such effects are consistent with the proposed mechanism of action. Progress of the SAR study is presented.

Yes1 kinase has been implicated as a potential therapeutic target in a number of cancers including melanomas, breast cancers, and rhabdomyosarcomas. Described here is the development of a robust and miniaturized biochemical assay for Yes1 kinase that was applied in a high throughput screen (HTS) of kinase-focused small molecule libraries. The HTS provided 144 (17% hit rate) small molecule compounds with IC50 values in the sub-micromolar range. Three of the most potent Yes1 inhibitors were then examined in a cell-based assay for inhibition of cell survival in rhabdomyosarcoma cell lines. Homology models of Yes1 were generated in active and inactive conformations, and docking of inhibitors supports binding to the active conformation (DFG-in) of Yes1. This is the first report of a large high throughput enzymatic activity screen for identification of Yes1 kinase inhibitors, thereby elucidating the polypharmacology of a variety of small molecules and clinical candidates.

Discovery of cancer genes through interrogation of genomic dosage is one of the major approaches in cancer research. In this study, we report that phosphodiesterase subtype 4D (PDE4D) gene was homozygously deleted in 198 cases of 5,569 primary solid tumors (3.56%), with most being internal microdeletions. Unexpectedly, the microdeletions did not result in loss of their gene products. Screening PDE4D expression in 11 different types of primary tumor samples ( n = 165) with immunohistochemistry staining revealed that its protein levels were up-regulated compared with corresponding nontransformed tissues. Importantly, depletion of endogenous PDE4D with three independent shRNAs caused apoptosis and growth inhibition in multiple types of cancer cells, including breast, lung, ovary, endometrium, gastric, and melanoma, which could be rescued by reexpression of PDE4D. We further showed that antitumor events triggered by PDE4D suppression were lineage-dependently associated with Bcl-2 interacting mediator of cell death (BIM) induction and microphthalmia-associated transcription factor (MITF) down-regulation. Furthermore, ectopic expression of the PDE4D short isoform, PDE4D2, enhanced the proliferation of cancer cells both in vitro and in vivo. Moreover, treatment of cancer cells with a unique specific PDE4D inhibitor, 26B, triggered massive cell death and growth retardation. Notably, these antineoplastic effects induced by either shRNAs or small molecule occurred preferentially in cancer cells but not in nonmalignant epithelial cells. These results suggest that although targeted by genomic homozygous microdeletions, PDE4D functions as a tumor-promoting factor and represents a unique targetable enzyme of cancer cells.