Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining

CANDI: A Web Server for Predicting Molecular Targets and Pathways of Cannabis-Based Therapeutics

Background

Cannabis sativa with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies.

Methods

To investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds.

Results

Our analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies.

Conclusions

By combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities.

Drug target prediction through deep learning functional representation of gene signatures

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

In Silico Transcriptome-based Screens Identify Epidermal Growth Factor Receptor Inhibitors as Therapeutics for Noise-induced Hearing Loss

Noise-Induced Hearing Loss (NIHL) represents a widespread disease for which no therapeutics have been approved by the Food and Drug Administration (FDA). Addressing the conspicuous void of efficacious in vitro or animal models for high throughput pharmacological screening, we utilized an in silico transcriptome-oriented drug screening strategy, unveiling 22 biological pathways and 64 promising small molecule candidates for NIHL protection. Afatinib and zorifertinib, both inhibitors of the Epidermal Growth Factor Receptor (EGFR), were validated for their protective efficacy against NIHL in experimental zebrafish and murine models. This protective effect was further confirmed with EGFR conditional knockout mice and EGF knockdown zebrafish, both demonstrating protection against NIHL. Molecular analysis using Western blot and kinome signaling arrays on adult mouse cochlear lysates unveiled the intricate involvement of several signaling pathways, with particular emphasis on EGFR and its downstream pathways being modulated by noise exposure and Zorifertinib treatment. Administered orally, Zorifertinib was successfully detected in the perilymph fluid of the inner ear in mice with favorable pharmacokinetic attributes. Zorifertinib, in conjunction with AZD5438 - a potent inhibitor of cyclin dependent kinase 2 - produced synergistic protection against NIHL in the zebrafish model. Collectively, our findings underscore the potential application of in silico transcriptome-based drug screening for diseases bereft of efficient screening models and posit EGFR inhibitors as promising therapeutic agents warranting clinical exploration for combatting NIHL.

Highlights

In silico transcriptome-based drug screens identify pathways and drugs against NIHL.EGFR signaling is activated by noise but reduced by zorifertinib in mouse cochleae.Afatinib, zorifertinib and EGFR knockout protect against NIHL in mice and zebrafish.Orally delivered zorifertinib has inner ear PK and synergizes with a CDK2 inhibitor.

Ligand-Guided Investigation of a Series of Formamidine-Based Thiuram Disulfides as Potential Dual-Inhibitors of COX-1and COX-2

A series of thiuram disulfides 1-6 which had been previously synthesized and characterized,^[1] were studied for their potential therapeutic properties. Target-fishing analyses through HitPick and SwissTarget prediction identified COX1 and COX2, which are essential biomolecules in cancer-related inflammations, as the possible targets for compounds 1 and 4 among all the compounds tested. These two proteins have enjoyed interest as targets for treating some neoplastic cancer types such as breast, colorectal, skin, pancreatic, haematological and head cancers. The inhibitory potency of 1 and 4 as lead anticancer drug candidates with dual-target ability against COX1 and COX2 was examined through molecular docking, molecular dynamics simulation and post-MD analyses such as binding energy calculation, RMSD, RMSF, and RoG. The two compounds had better docking scores and binding energies than the known inhibitors of COX1 and COX2. Insights from the RMSD, RMSF, and RoG suggested that both 1 and 4 showed observable influence on the structural stability of these targets throughout the simulation. The reported observations of the effects of 1 and 4 on the structures of COX1 and COX2 indicate their probable inhibitory properties against these target proteins and their potential as lead anticancer drug candidates.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

Discovery of Novel eEF2K Inhibitors Using HTS Fingerprint Generated from Predicted Profiling of Compound-Protein Interactions

Background: Eukaryotic elongation factor 2 kinase (eEF2K) regulates the elongation stage of protein synthesis by phosphorylating eEF2, a process related to various diseases including cancer and cardiovascular and neurodegenerative diseases. In this study, we describe the identification of novel eEF2K inhibitors using high-throughput screening fingerprints (HTSFP) generated from predicted profiling of compound-protein interactions (CPIs). Methods: We utilized computationally generated HTSFPs referred to as chemical genomics-based fingerprint (CGBFP). Generally, HTSFPs are generated from multiple biochemical or cell-based assay data. On the other hand, CGBFPs are generated from computational prediction of CPIs using the Chemical Genomics-Based Virtual Screening (CGBVS) method. Therefore, CGBFPs do not have missing information mainly caused by the absence of assay data. Results: Chemogenomics-Based Similarity Profiling (CGBSP) of the screening library (2.6 million compounds) yielded 27 compounds which were evaluated for in vitro eEF2K inhibitory activity. Three compounds with interesting results were identified. Compounds 2 (IC50 = 11.05 μM) and 4 (IC50 = 43.54 μM) are thieno[2,3-b]pyridine derivatives that have the same scaffolds with a known eEF2K inhibitor, while compound 13 (IC50 = 70.13 μM) was a new thiophene-2-amine-type eEF2K inhibitor. Conclusions: CGBSP supplied an efficient strategy in the identification of novel eEF2K inhibitors and provided useful scaffolds for optimization.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K_i, K_d, IC₅₀ and EC₅₀ data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC₅₀ data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC₅₀ units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC₅₀ units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC₅₀ units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

To date, PDAC remains the cancer having the worst prognosis with mortality rates constantly on the rise. Efficient cures are still absent, despite all attempts to understand the aggressive physiopathology underlying this disease. A major stumbling block is the outdated preclinical modeling strategies applied in assessing effectiveness of novel anticancer therapeutics. Current in vitro preclinical models have a low fidelity to mimic the exact architectural and functional complexity of PDAC tumor found in human set, due to the lack of major components such as immune system and tumor microenvironment with its associated chemical and mechanical signals. The existing PDAC preclinical platforms are still far from being reliable and trustworthy to guarantee the success of a drug in clinical trials. Therefore, there is an urgent demand to innovate novel in vitro preclinical models that mirrors with precision tumor-microenvironment interface, pressure of immune system, and molecular and morphological aspects of the PDAC normally experienced within the living organ. This review outlines the traditional preclinical models of PDAC namely 2D cell lines, genetically engineered mice, and xenografts, and describing the present famous approach of 3D organoids. We offer a detailed narration of the pros and cons of each model system. Finally, we suggest the incorporation of two off-center newly born techniques named 3D bio-printing and organs-on-chip and discuss the potentials of swine models and in silico tools, as powerful new tools able to transform PDAC preclinical modeling to a whole new level and open new gates in personalized medicine.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Computational Drug Repurposing: Current Trends

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Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

A platform for target prediction of phenotypic screening hit molecules

Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database.

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An algorithm to predict the molecular target(s) of phenotypic hits against TB.

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Uses information based on the ligand and protein structure.

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Allow visualisation of proposed binding pose.

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Web interface developed.

Reliable Target Prediction of Bioactive Molecules Based on Chemical Similarity Without Employing Statistical Methods

The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

TCM-ADMEpred: A novel strategy for poly-pharmacokinetics prediction of traditional Chinese medicine based on single constituent pharmacokinetics, structural similarity, and mathematical modeling

ETHNOPHARMACOLOGICAL RELEVANCE

Yuanhu Zhitong prescription (YZP) is a commonly used and relatively simple clinical herb preparation recorded in the China Pharmacopoeia. It contains Corydalis yanhusuo (Chinese name, Yanhusuo [YH]) and Angelica dahurica (Hoffm.) (Chinese name, Baizhi [BZ]), and has a long history of use in traditional Chinese medicine (TCM) for the treatment of stomach pain, hypochondriac pain, headache, and dysmenorrhea.

AIM OF THE STUDY

A TCM-ADMEpred method is developed for novel strategy for poly-pharmacokinetics prediction of TCM. To predict the pharmacokinetic characteristics of the main YZP constituents in rat plasma using in silico models, based on the theory that structurally similar constituents show similar pharmacokinetic properties. This approach may facilitate in silico prediction of the pharmacokinetics of TCM.

MATERIALS AND METHODS

A robust platform using ultra-performance liquid chromatography coupled with triple quadrupole electrospray tandem mass spectrometry (UPLC-ESI-MS/MS) was developed and validated for simultaneous determination of seven active YZP constituents in rat plasma. These seven compounds were divided into two structural classes, alkaloids and coumarins. The correlation between AUC profiles within a structural class was expressed as Γ⁺, and this variable was used to develop two novel in silico models to predict constituent AUC values. The pharmacokinetics of tetrahydropalmatine, tetrahydroberberine, and corydaline following YZP administration were predicted using the Γ⁺-values of α-allocryptopine observed following YH administration, while those of imperatorin and isoimperatorin following BZ administration were predicted using the Γ⁺-values of byakangelicin observed following YZP administration.

RESULTS

The UPLC-ESI-MS/MS method was successfully used to evaluate pharmacokinetic parameters after oral YZP, YH, or BZ administration. Our findings showed that co-administration of YH and BZ increased the AUC of four alkaloid constituents and reduced the AUC of three coumarin constituents, which might provide a scientific rationale for co-administering these herbs clinically as a YZP preparation, thus increasing their efficacy and reducing toxicity. The AUC values of imperatorin and isoimperatorin were predicted 3 h after oral BZ administration, with the bias ratios between the theoretical values and the observed experimental values ranging from 0.61% to 11.4%, and average bias ratios of 5.8% and 8.0%, respectively. The AUC values of tetrahydropalmatine, tetrahydroberberine, and corydaline were predicted 3 h after oral YZP administration, with bias ratios ranging from 3.7% to 46.4%, and average bias ratios of 23.8%, 15.4%, and 25.8%, respectively.

CONCLUSION

The UPLC-ESI-MS/MS method was successfully applied to pharmacokinetic evaluations after oral administration of YZP, YH, and BZ to rats. The Γ⁺ variable was used to express the correlation between the AUC profiles of structurally similar compounds. This facilitated the development of an in silico model that was used to predict the AUC of three alkaloids in YZP and of two coumarins in BZ. Calculation of the bias ratios between the predicted and experimental values suggested that this in silico model provided a viable approach for the prediction of TCM pharmacokinetics.

CSgator: an integrated web platform for compound set analysis

Drug discovery typically involves investigation of a set of compounds (e.g. drug screening hits) in terms of target, disease, and bioactivity. CSgator is a comprehensive analytic tool for set-wise interpretation of compounds. It has two unique analytic features of Compound Set Enrichment Analysis (CSEA) and Compound Cluster Analysis (CCA), which allows batch analysis of compound set in terms of (i) target, (ii) bioactivity, (iii) disease, and (iv) structure. CSEA and CCA present enriched profiles of targets and bioactivities in a compound set, which leads to novel insights on underlying drug mode-of-action, and potential targets. Notably, we propose a novel concept of 'Hit Enriched Assays", i.e. bioassays of which hits are enriched among a given set of compounds. As an example, we show its utility in revealing drug mode-of-action or identifying hidden targets for anti-lymphangiogenesis screening hits. CSgator is available at http://csgator.ewha.ac.kr , and most analytic results are downloadable.

Repurposing approach identifies new treatment options for invasive fungal disease

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds

Aim:

Computational exploration of small-molecule-based relationships between target proteins from different families.

Materials & methods:

Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data.

Results:

A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets.

Conclusion:

Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available.

Relationships between proteins are usually studied by comparing their sequences and functions. However, in addition to biological relationships, chemical links between proteins can also be established by searching for active compounds they share. If proteins have active compounds in common, they are likely to interact with small molecules in similar ways, which provides important clues for drug discovery. Therefore, we have systematically searched for unexpected compound-based relationships between proteins.

Shown here are exemplary small molecules that are active against two targets with different functions. Thus, these compounds establish an unexpected chemical/ligand-binding relationship between these targets.

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools.

Computational approaches in target identification and drug discovery

In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM's diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients' target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ's cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the "multi-component, multi-target and multi-pathway" combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM's molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem's Bioassay Repository

Molecular profiling efforts aim at characterizing the biological actions of small molecules by screening them in hundreds of different biochemical and/or cell-based assays. Together, these assays yield a rich data landscape of target-based and phenotypic effects of the tested compounds. However, submitting an entire compound library to a molecular profiling panel can easily become cost-prohibitive. Here, we make use of historical screening assays to create comprehensive bioactivity profiles for more than 300 000 small molecules. These bioactivity profiles, termed PubChem high-throughput screening fingerprints (PubChem HTSFPs), report small molecule activities in 243 different PubChem bioassays. Although the assays originate from originally independently pursued drug or probe discovery projects, we demonstrate their value as molecular signatures when used in combination. We use these PubChem HTSFPs as molecular descriptors in hit expansion experiments for 33 different targets and phenotypes, showing that, on average, they lead to 27 times as many hits in a set of 1000 chosen molecules as a random screening subset of the same size (average ROC score: 0.82). Moreover, we demonstrate that PubChem HTSFPs retrieve hits that are structurally diverse and distinct from active compounds retrieved by chemical similarity-based hit expansion methods. PubChem HTSFPs are made freely available for the chemical biology research community.

A survey on the computational approaches to identify drug targets in the postgenomic era

Identifying drug targets plays essential roles in designing new drugs and combating diseases. Unfortunately, our current knowledge about drug targets is far from comprehensive. Screening drug targets in the lab is an expensive and time-consuming procedure. In the past decade, the accumulation of various types of omics data makes it possible to develop computational approaches to predict drug targets. In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of omics data and drug property data.

Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

Determining the targets of compounds identified in cell-based high-throughput chemical screens is a critical step for downstream drug development and understanding of compound mechanism of action. However, current computational target prediction approaches like chemical similarity database searches are limited to single or sequential ligand analyses, which limits their ability to accurately deconvolve a large number of compounds that often have chemically diverse structures. Here, we have developed a new computational drug target prediction method, called CSNAP that is based on chemical similarity networks. By clustering diverse chemical structures into distinct sub-networks corresponding to chemotypes, we show that CSNAP improves target prediction accuracy and consistency over a board range of drug classes. We further coupled CSNAP to a mitotic database and successfully determined the major mitotic drug targets of a diverse compound set identified in a cell-based chemical screen. We demonstrate that CSNAP can easily integrate with diverse knowledge-based databases for on/off target prediction and post-target validation, thus broadening its applicability for identifying the targets of bioactive compounds from a wide range of chemical screens.

The opportunities of mining historical and collective data in drug discovery

Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery.

A screening pattern recognition method finds new and divergent targets for drugs and natural products

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 μM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

Using information from historical high-throughput screens to predict active compounds

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion

Background

Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.

Results

We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.

Conclusions

With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Pathway analysis for drug repositioning based on public database mining

Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.

Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses

High-throughput screening allows rapid identification of new candidate compounds for biological probe or drug development. Here, we describe a principled method to generate "assay performance profiles" for individual compounds that can serve as a basis for similarity searches and cluster analyses. Our method overcomes three challenges associated with generating robust assay performance profiles: (1) we transform data, allowing us to build profiles from assays having diverse dynamic ranges and variability; (2) we apply appropriate mathematical principles to handle missing data; and (3) we mitigate the fact that loss-of-signal assay measurements may not distinguish between multiple mechanisms that can lead to certain phenotypes (e.g., cell death). Our method connected compounds with similar mechanisms of action, enabling prediction of new targets and mechanisms both for known bioactives and for compounds emerging from new screens. Furthermore, we used Bayesian modeling of promiscuous compounds to distinguish between broadly bioactive and narrowly bioactive compound communities. Several examples illustrate the utility of our method to support mechanism-of-action studies in probe development and target identification projects.

The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens

A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor-α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

Predicting drug-target interactions using probabilistic matrix factorization

Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

Making Models Work: Library Annotation through Phenoclustering

For the chemical biologist, the promise of the post-genomic era has yet to be fulfilled. In the past decade, a flurry of phenotype-based chemical genetic screens in in vivo and cultured cell models have yielded numerous small molecules with interesting biological properties with potential to reveal plethora of novel insights. However, these screens have also led to the bottleneck of target identification. This article will focus on recent progress in phenoclustering in various model systems as an option for target identification.

Web search and data mining of natural products and their bioactivities in PubChem

Natural products, as major resources for drug discovery historically, are gaining more attentions recently due to the advancement in genomic sequencing and other technologies, which makes them attractive and amenable to drug candidate screening. Collecting and mining the bioactivity information of natural products are extremely important for accelerating drug development process by reducing cost. Lately, a number of publicly accessible databases have been established to facilitate the access to the chemical biology data for small molecules including natural products. Thus, it is imperative for scientists in related fields to exploit these resources in order to expedite their researches on natural products as drug leads/candidates for disease treatment. PubChem, as a public database, contains large amounts of natural products associated with bioactivity data. In this review, we introduce the information system provided at PubChem, and systematically describe the applications for a set of PubChem web services for rapid data retrieval, analysis, and downloading of natural products. We hope this work can serve as a starting point for the researchers to perform data mining on natural products using PubChem.

A broad activity screen in support of a chemogenomic map for kinase signalling research and drug discovery

Despite the development of a number of efficacious kinase inhibitors, the strategies for rational design of these compounds have been limited by target promiscuity. In an effort to better understand the nature of kinase inhibition across the kinome, especially as it relates to off-target effects, we screened a well-defined collection of kinase inhibitors using biochemical assays for inhibitory activity against 234 active human kinases and kinase complexes, representing all branches of the kinome tree. For our study we employed 158 small molecules initially identified in the literature as potent and specific inhibitors of kinases important as therapeutic targets and/or signal transduction regulators. Hierarchical clustering of these benchmark kinase inhibitors on the basis of their kinome activity profiles illustrates how they relate to chemical structure similarities and provides new insights into inhibitor specificity and potential applications for probing new targets. Using this broad dataset, we provide a framework for assessing polypharmacology. We not only discover likely off-target inhibitor activities and recommend specific inhibitors for existing targets, but also identify potential new uses for known small molecules.

Cheminformatics at the interface of medicinal chemistry and proteomics

Multiple factors have to be optimized in the course of a drug discovery project. Traditionally this includes potency on a single target, eventually specificity as well as the pharmacokinetic, physicochemical and the safety profile. Recently an additional dimension has been added by realizing that the therapeutic outcome of a drug is often determined not only by its activity on a single target but also by its activity profile across a variety of biological targets. To address the polypharmacology of drug candidates many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. To extract useful information computational methods at the interface of proteomics and cheminformatics are indispensable. This review will focus on some recent developments in this field. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

A computational drug-target network for yuanhu zhitong prescription

Yuanhu Zhitong prescription (YZP) is a typical and relatively simple traditional Chinese medicine (TCM), widely used in the clinical treatment of headache, gastralgia, and dysmenorrhea. However, the underlying molecular mechanism of action of YZP is not clear. In this study, based on the previous chemical and metabolite analysis, a complex approach including the prediction of the structure of metabolite, high-throughput in silico screening, and network reconstruction and analysis was developed to obtain a computational drug-target network for YZP. This was followed by a functional and pathway analysis by ClueGO to determine some of the pharmacologic activities. Further, two new pharmacologic actions, antidepressant and antianxiety, of YZP were validated by animal experiments using zebrafish and mice models. The forced swimming test and the tail suspension test demonstrated that YZP at the doses of 4 mg/kg and 8 mg/kg had better antidepressive activity when compared with the control group. The anxiolytic activity experiment showed that YZP at the doses of 100 mg/L, 150 mg/L, and 200 mg/L had significant decrease in diving compared to controls. These results not only shed light on the better understanding of the molecular mechanisms of YZP for curing diseases, but also provide some evidence for exploring the classic TCM formulas for new clinical application.