ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning.

Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights. Identifying key compounds in patents is essential to finding novel lead compounds. This study collected a comprehensive data set comprising 1555 patents, encompassing 1000 key compounds, to explore innovative approaches for predicting these key compounds. Our novel PatentNetML framework integrated network science and machine learning algorithms, combining network measures, ADMET properties, and physicochemical properties, to construct robust classification models to identify key compounds. Through a model interpretation and an analysis of three compelling case studies, we showcase the potential of PatentNetML in unveiling hidden patterns and connections within diverse patents. While our framework is pioneering, we acknowledge its limitations when applied to patents that deviate from the assumed central pattern. This work serves as a promising foundation for future research endeavors aimed at efficiently identifying promising drug candidates and expediting drug discovery in the pharmaceutical industry.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences.

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

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.

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.

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.

TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine.

Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .

Appraisal of treatment outcomes in integrative medicine using metabonomics: Taking non-alcoholic fatty liver disease with spleen deficiency syndrome as an example.

OBJECTIVE: Appraisal of treatment outcomes in integrative medicine is a challenge due to a gap between the concepts of Western medicine (WM) disease and traditional Chinese medicine (TCM) syndrome. This study presents an approach for the appraisal of integrative medicine that is based on targeted metabolomics. We use non-alcoholic fatty liver disease with spleen deficiency syndrome as a test case. METHODS: A patient-reported outcome (PRO) scale was developed based on literature review, Delphi consensus survey, and reliability and validity test, to quantitatively evaluate spleen deficiency syndrome. Then, a metabonomic foundation for the treatment of non-alcoholic fatty liver disease with spleen deficiency syndrome was identified via a longitudinal interventional trial and targeted metabolomics. Finally, an integrated appraisal model was established by identifying metabolites that responded in the treatment of WM disease and TCM syndrome as positive outcomes and using other aspects of the metabonomic foundation as independent variables. RESULTS: Ten symptoms and signs were included in the spleen deficiency PRO scale. The internal reliability, content validity, discriminative validity and structural validity of the scale were all qualified. Based on treatment responses to treatments for WM disease (homeostasis model assessment of insulin resistance) or TCM syndrome (spleen deficiency PRO scale score) from a previous randomized controlled trial, two cohorts comprised of 30 participants each were established for targeted metabolomics detection. Twenty-five metabolites were found to be involved in successful treatment outcomes to both WM and TCM, following quantitative comparison and multivariate analysis. Finally, the model of the integrated appraisal system was exploratively established using binary logistic regression; it included 9 core metabolites and had the prediction probability of 83.3%. CONCLUSION: This study presented a new and comprehensive research route for integrative appraisal of treatment outcomes for WM disease and TCM syndrome. Critical research techniques used in this research included the development of a TCM syndrome assessment tool, a longitudinal interventional trial with verified TCM treatment, identification of homogeneous metabolites, and statistical modeling.

The improvement of nonalcoholic steatohepatitis by Poria cocos polysaccharides associated with gut microbiota and NF-κB/CCL3/CCR1 axis.

BACKGROUND: Nonalcoholic steatohepatitis (NASH) has been linked to inflammation induced by intestinal microbiota. Poria cocos polysaccharides (PCP) possesses anti-inflammation and immunomodulation functions; however, its preventive effects against NASH and potential mechanisms need to be explored. METHODS: The composition of PCP was determined using ion chromatography. C57BL/6 mice were administered the methionine and choline deficient (MCD) diet for 4 weeks to establish the NASH model or methionine-choline-sufficient (MCS) diet to serve as the control. Mice were assigned to the MCS group, MCD group, low-dose PCP (LP) group, and high-dose PCP (HP) group, and were administered the corresponding medications via gavage. Serum biochemical index analysis and liver histopathology examination were performed to verify the successful establishment of NASH model and to evaluate the efficacy of PCP. The composition of intestinal bacteria was profiled through 16S rRNA gene sequencing. Hepatic RNA sequencing (RNA-Seq) was performed to explore the potential mechanisms, which were further confirmed using qPCR, western blot, and immunohistochemistry. RESULTS: PCP consists of glucose, galactose, mannose, D-glucosamine hydrochloride, xylose, arabinose, and fucose. PCP could significantly alleviate symptoms of NASH, including histological liver damage, impaired hepatic function, and increased oxidative stress. Meanwhile, HP could reshape the composition of intestinal bacteria by significantly increasing the relative abundance of Faecalibaculum and decreasing the level of endotoxin load derived from gut bacteria. PCP could also downregulate the expression of pathways associated with immunity and inflammation, including the chemokine signaling pathway, Toll-like receptor signaling pathway, and NF-kappa B signaling pathway. The expression levels of CCL3 and CCR1 (involved in the chemokine signaling pathway), Tlr4, Cd11b, and NF-κb (involved in the NF-kappa B signaling pathway), and Tnf-α (involved in the TNF signaling pathway) were significantly reduced in the HP group compared to the MCD group. CONCLUSIONS: PCP could prevent the development of NASH, which may be associated with the modulation of intestinal microbiota and the downregulation of the NF-κB/CCL3/CCR1 axis.

Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.

Drug-drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.

Detecting Critical Functional Ingredients Group and Mechanism of Xuebijing Injection in Treating Sepsis.

Sepsis is a systemic inflammatory reaction caused by various infectious or noninfectious factors, which can lead to shock, multiple organ dysfunction syndrome, and death. It is one of the common complications and a main cause of death in critically ill patients. At present, the treatments of sepsis are mainly focused on the controlling of inflammatory response and reduction of various organ function damage, including anti-infection, hormones, mechanical ventilation, nutritional support, and traditional Chinese medicine (TCM). Among them, Xuebijing injection (XBJI) is an important derivative of TCM, which is widely used in clinical research. However, the molecular mechanism of XBJI on sepsis is still not clear. The mechanism of treatment of "bacteria, poison and inflammation" and the effects of multi-ingredient, multi-target, and multi-pathway have still not been clarified. For solving this issue, we designed a new systems pharmacology strategy which combines target genes of XBJI and the pathogenetic genes of sepsis to construct functional response space (FRS). The key response proteins in the FRS were determined by using a novel node importance calculation method and were condensed by a dynamic programming strategy to conduct the critical functional ingredients group (CFIG). The results showed that enriched pathways of key response proteins selected from FRS could cover 95.83% of the enriched pathways of reference targets, which were defined as the intersections of ingredient targets and pathogenetic genes. The targets of the optimized CFIG with 60 ingredients could be enriched into 182 pathways which covered 81.58% of 152 pathways of 1,606 pathogenetic genes. The prediction of CFIG targets showed that the CFIG of XBJI could affect sepsis synergistically through genes such as TAK1, TNF-α, IL-1ß, and MEK1 in the pathways of MAPK, NF-κB, PI3K-AKT, Toll-like receptor, and tumor necrosis factor signaling. Finally, the effects of apigenin, baicalein, and luteolin were evaluated by in vitro experiments and were proved to be effective in reducing the production of intracellular reactive oxygen species in lipopolysaccharide-stimulated RAW264.7 cells, significantly. These results indicate that the novel integrative model can promote reliability and accuracy on depicting the CFIGs in XBJI and figure out a methodological coordinate for simplicity, mechanism analysis, and secondary development of formulas in TCM.

Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion.

In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.

Efficacy of Huaiqihuang granules as adjuvant therapy for bronchial asthma in children: a real-world study. / æ§æžé»„é¢—ç²’è¾ åŠ©æ²»ç–—å„¿ç«¥æ”¯æ°”ç®¡å“®å–˜ç–—æ•ˆçš„çœŸå®žä¸–ç•Œç ”ç©¶.

OBJECTIVES: To study the efficacy of Huaiqihuang granules as adjuvant therapy for bronchial asthma in children. METHODS: A multicenter, prospective, and registered real-world study was performed for the children, aged 2-5 years, who had a confirmed diagnosis of bronchial asthma in the outpatient service of 21 hospitals in China. Among these children, the children treated with medications for long-term asthma control (inhaled corticosteroid and/or leukotriene receptor antagonist) without Huaiqihuang granules were enrolled as the control treatment group, and those treated with medications for long-term asthma control combined with Huaiqihuang granules were enrolled as the combined treatment group. The medical data of all children were collected. Outpatient or telephone follow-up was performed at weeks 4, 8, 12, 20, 28, and 36 after treatment, including asthma attacks and rhinitis symptoms. A statistical analysis was performed for the changes in these indices. RESULTS: There was no significant difference in the frequency of asthma attacks or rhinitis attacks between the two groups before treatment (P>0.05). After treatment, the combined treatment group had significantly lower frequencies of asthma attacks, severe asthma attacks, and rhinitis attacks compared with the control treatment group (P<0.05). There was no signification difference in the incidence rate of adverse reactions between the two groups (P=0.667). CONCLUSIONS: Huaiqihuang granules in addition to medications for long-term asthma control can alleviate the symptoms of bronchial asthma and rhinitis and improve the level of asthma control in children with bronchial asthma, with good safety and little adverse effect. Citation.

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.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors.

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds.

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC50 of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

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.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied. AIM OF STUDY: A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC. MATERIALS AND METHODS: Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats. RESULTS: Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro. CONCLUSION: The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

Benchmarking the mechanisms of frequent hitters: limitation of PAINS alerts.

In 2010, the pan-assay interference compounds (PAINS) rule was proposed to identify false-positive compounds, especially frequent hitters (FHs), in biological screening campaigns, and has rapidly become an essential component in drug design. However, the specific mechanisms remain unknown, and the result validation and follow-up processing schemes are still unclear. In this review, a large benchmark collection of >600,000 compounds sourced from databases and the literature, including six common false-positive mechanisms, was used to evaluate the detection ability of PAINS. In addition, 400 million purchasable molecules from the ZINC database were also applied to PAINS screening. The results indicate that the PAINS rule is not suitable for the screening of all types of false-positive results and needs more improvement.

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.