Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model.

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 µM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

Improving structure-based virtual screening performance via learning from scoring function components.

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study.

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

Scopy: an integrated negative design python library for desirable HTS/VS database design.

BACKGROUND: High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION: Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Computationally guided discovery of novel non-steroidal AR-GR dual antagonists demonstrating potency against antiandrogen resistance.

As a major class of medicine for treating the lethal type of castration-resistant prostate cancer (PCa), long-term use of androgen receptor (AR) antagonists commonly leads to antiandrogen resistance. When AR signaling pathway is blocked by AR-targeted therapy, glucocorticoid receptor (GR) could compensate for AR function especially at the late stage of PCa. AR-GR dual antagonist is expected to be a good solution for this situation. Nevertheless, no effective non-steroidal AR-GR dual antagonist has been reported so far. In this study, an AR-GR dual binder H18 was first discovered by combining structure-based virtual screening and biological evaluation. Then with the aid of computationally guided design, the AR-GR dual antagonist HD57 was finally identified with antagonistic activity towards both AR (IC50 = 0.394 µM) and GR (IC50 = 17.81 µM). Moreover, HD57 could effectively antagonize various clinically relevant AR mutants. Further molecular dynamics simulation provided more atomic insights into the mode of action of HD57. Our research presents an efficient and rational strategy for discovering novel AR-GR dual antagonists, and the new scaffold provides important clues for the development of novel therapeutics for castration-resistant PCa.

Discovery of novel MIF inhibitors that attenuate microglial inflammatory activation by structures-based virtual screening and in vitro bioassays.

Macrophage migration inhibitory factor (MIF) is a pluripotent pro-inflammatory cytokine and is related to acute and chronic inflammatory responses, immune disorders, tumors, and other diseases. In this study, an integrated virtual screening strategy and bioassays were used to search for potent MIF inhibitors. Twelve compounds with better bioactivity than the prototypical MIF-inhibitor ISO-1 (IC50 = 14.41 µM) were identified by an in vitro enzymatic activity assay. Structural analysis revealed that these inhibitors have novel structural scaffolds. Compound 11 was then chosen for further characterization in vitro, and it exhibited marked anti-inflammatory efficacy in LPS-activated BV-2 microglial cells by suppressing the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPKs). Our findings suggest that MIF may be involved in the regulation of microglial inflammatory activation and that small-molecule MIF inhibitors may serve as promising therapeutic agents for neuroinflammatory diseases.

Novel Caspase-1 inhibitor CZL80 improves neurological function in mice after progressive ischemic stroke within a long therapeutic time-window.

Progressive ischemic stroke (PIS) is featured by progressive neurological dysfunction after ischemia. Ischemia-evoked neuroinflammation is implicated in the progressive brain injury after cerebral ischemia, while Caspase-1, an active component of inflammasome, exaggerates ischemic brain injury. Current Caspase-1 inhibitors are inadequate in safety and druggability. Here, we investigated the efficacy of CZL80, a novel Caspase-1 inhibitor, in mice with PIS. Mice and Caspase-1-/- mice were subjected to photothrombotic (PT)-induced cerebral ischemia. CZL80 (10, 30 mg·kg-1·d-1, i.p.) was administered for one week after PT onset. The transient and the progressive neurological dysfunction (as foot faults in the grid-walking task and forelimb symmetry in the cylinder task) was assessed on Day1 and Day4-7, respectively, after PT onset. Treatment with CZL80 (30 mg/kg) during Day1-7 significantly reduced the progressive, but not the transient neurological dysfunction. Furthermore, we showed that CZL80 administered on Day4-7, when the progressive neurological dysfunction occurred, produced significant beneficial effects against PIS, suggesting an extended therapeutic time-window. CZL80 administration could improve the neurological function even as late as Day43 after PT. In Caspase-1-/- mice with PIS, the beneficial effects of CZL80 were abolished. We found that Caspase-1 was upregulated during Day4-7 after PT and predominantly located in activated microglia, which was coincided with the progressive neurological deficits, and attenuated by CZL80. We showed that CZL80 administration did not reduce the infarct volume, but significantly suppressed microglia activation in the peri-infarct cortex, suggesting the involvement of microglial inflammasome in the pathology of PIS. Taken together, this study demonstrates that Caspase-1 is required for the progressive neurological dysfunction in PIS. CZL80 is a promising drug to promote the neurological recovery in PIS by inhibiting Caspase-1 within a long therapeutic time-window.

Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays.

Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.

Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening.

Decaprenylphosphoryl-ß-D-ribose oxidase (DprE1) plays important roles in the biosynthesis of mycobacterium cell wall. DprE1 inhibitors have shown great potentials in the development of new regimens for tuberculosis (TB) treatment. In this study, an integrated molecular modeling strategy, which combined computational bioactivity fingerprints and structure-based virtual screening, was employed to identify potential DprE1 inhibitors. Two lead compounds (B2 and H3) that could inhibit DprE1 and thus kill Mycobacterium smegmatis in vitro were identified. Moreover, compound H3 showed potent inhibitory activity against Mycobacterium tuberculosis in vitro (MICMtb = 1.25 µM) and low cytotoxicity against mouse embryo fibroblast NIH-3T3 cells. Our research provided an effective strategy to discover novel anti-TB lead compounds.

Discovery of a novel nonsteroidal selective glucocorticoid receptor modulator by virtual screening and bioassays.

Synthetic glucocorticoids (GCs) have been widely used in the treatment of a broad range of inflammatory diseases, but their clinic use is limited by undesired side effects such as metabolic disorders, osteoporosis, skin and muscle atrophies, mood disorders and hypothalamic-pituitary-adrenal (HPA) axis suppression. Selective glucocorticoid receptor modulators (SGRMs) are expected to have promising anti-inflammatory efficacy but with fewer side effects caused by GCs. Here, we reported HT-15, a prospective SGRM discovered by structure-based virtual screening (VS) and bioassays. HT-15 can selectively act on the NF-κB/AP1-mediated transrepression function of glucocorticoid receptor (GR) and repress the expression of pro-inflammation cytokines (i.e., IL-1ß, IL-6, COX-2, and CCL-2) as effectively as dexamethasone (Dex). Compared with Dex, HT-15 shows less transactivation potency that is associated with the main adverse effects of synthetic GCs, and no cross activities with other nuclear receptors. Furthermore, HT-15 exhibits very weak inhibition on the ratio of OPG/RANKL. Therefore, it may reduce the side effects induced by normal GCs. The bioactive compound HT-15 can serve as a starting point for the development of novel therapeutics for high dose or long-term anti-inflammatory treatment.