MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability. To address these limitations, we propose miCGR, an end-to-end deep learning framework for predicting functional miRNA targets. MiCGR employs 2D convolutional neural networks alongside an enhanced Chaos Game Representation (CGR) of both miRNA sequences and their candidate target site (CTS) on mRNA. This advanced CGR transforms genetic sequences into informative 2D graphical representations based on sequence composition and subsequence frequencies, and explicitly incorporates important prior knowledge of seed regions and subsequence positions. Unlike one-dimensional methods based solely on sequence characters, this approach identifies functional motifs within sequences, even if they are distant in the original sequences. Our model outperforms existing methods in predicting functional targets at both the site and gene levels. To enhance interpretability, we incorporate Shapley value analysis for each subsequence within both miRNA sequences and their target sites, allowing miCGR to achieve improved accuracy, particularly with more lenient CTS selection criteria. Finally, two case studies demonstrate the practical applicability of miCGR, highlighting its potential to provide insights for optimizing artificial miRNA analogs that surpass endogenous counterparts.

This study investigates global ionosphere responses and driving mechanisms during the May 2024 super geomagnetic storm, using data from 6268 GNSS stations, multi-source observations from ground- and satellite -based platforms, and empirical-physical models. It focuses on particle composition and temperature variation, ionospheric storm, large-scale traveling ionospheric disturbance (LSTID), scintillation, and vertical disturbance. Results show that enhanced polar energy input during the storm increased electron density (Ne) and electron/ion temperatures in the nighttime F2 layer, with electron heating concentrated above 300 km and reaching up to 4000 K, while electron precipitation occurred into the E layer, and broader ion heating across the F2–E layers, reaching up to 2000 K. These energy inputs further drove Ne gradient variations and turbulence, synchronizing high-latitude LSTIDs, scintillations, and ionospheric storms, with stronger and longer disturbances in the Northern Hemisphere (NH). Multiple global disturbances exhibited spatial variability and coupling structures reflected in intensity, meridional propagation, and multi-scale co-evolution. The strongest responses were observed in the high-latitude F2 layer, with evident time delays during equatorward propagation, following a decrease–then–increase pattern with decreasing latitude. High-latitude enhancements were linked to Joule heating, particle precipitation, and storm-induced Ne variations, weakened during propagation to mid-latitudes due to ion drag and viscosity, but reintensified at lower latitudes due to penetration electric field (PEF) and diurnal solar radiation coupling. LSTIDs driven by atmospheric gravity waves responded more rapidly and intensely to energy input than scintillations. During the main phase, over North America below 60°N and Southern Hemisphere (SH) at magnetic latitudes above 20°S, the eastward PEF, an ΣO/N₂ increase to 1.4, and O⁺ enhancement jointly elevated Ne by 63.8%, causing the strongest positive storm. The strongest disturbances occurred in the Americas, featuring LSTIDs with meridional velocities (Vm) of 840–910 m/s and amplitudes (Amp) over 25 TECU, along with banded scintillations (Amp>1.3 TECU/min), and vertical disturbances propagating upward at 22–27 m/s, lifting the entire ionosphere. Eurasia experienced notable negative storms (Ne reduced by 53.2%), due to ΣO/N₂ depletion to below 0.2, NO⁺ enhancement, and westward PEF. European LSTIDs were short-period, densely overlapping (Vm>1250 m/s, Amp<13 TECU, period<40 minutes). Over Eurasia, ionosondes recorded a marked Ne decrease by up to 8.2 × 10⁵ el/cm³, nearly destroyed ionospheric layering, and observed downward-propagating disturbances at 21–23 m/s. Regarding the vertical structure, global peak Ne variations correlated closely with ionospheric storms and ΣO/N₂ changes, while peak heights exhibited global uplift driven by plasma drifts and thermospheric expansion. Differences in LSTIDs propagation characteristics driven by auroral electrojets and electric fields, AGWs induced by Joule heating and particle precipitation, hemispheric asymmetries in energy deposition and the Coriolis force, intensified summer-to-winter seasonal circulation and winds, and ion-drag differences modulated by local solar radiation were further analyzed. In the recovery phase, global negative storms emerged due to ΣO/N₂ depletion, with no notable LSTIDs or scintillations observed after 14 UT on May 11 as storm energy input had weakened. However, in the Asia-Pacific, increased ΣO/N₂ and solar radiation caused a re-intensification of disturbances that persisted until 21:30 UT.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Biometrics are widely used for user identification/authentication, but the fact has rarely been noticed that general capacitive touchscreens can reveal user identities by touch signals. This paper proposes a new biometric method with inherent liveness detection for reliable user recognition based on the cardiac signal captured by the capacitive touchscreen, namely Capacitive Plethysmogram (CPG). And a systematic framework is designed for CPG collection, processing, and exploitation to identify users. Specifically, since the finger usually forms capacitors with multiple sensing electrodes during touching, we can extract several CPG signals simultaneously from the screen output. Then we propose a series of preprocessing algorithms to filter CPG for signal quality enhancement. Finally, to further leverage filtered CPG signals and extract efficient features for identifying users, we build an encoder based on 3D attention CNN and metric learning. Experimental results demonstrate that the proposed method can achieve an average accuracy of 96.73%, FAR of 3.03%, and FRR of 7.35% in the laboratory environment, which reveals the potential of CPG for user privacy protection and data security on various devices laced with capacitive touchscreens.

Using the global navigation satellite system (GNSS) data from the Hong Kong region, this study comprehensively investigates the ionospheric irregularities responses to strong geomagnetic storms over the past two solar cycles 2001–2020. Based on the geomagnetic index Dst, a total of 64 strong storms are confirmed during 2001–2020. Statistical results indicate that for the total 64 strong storms, only 20 storms are considered to trigger irregular occurrences. When the occurrence local time (LT) of the minimum dDst (dDst$_{\mathrm {min}}$) is in 10:00–14:00 LT, no ionospheric irregularities occurred at nighttime although there is a total of 14 strong storms, while that of dDst min is in the nighttime of 18:00–21:00 LT, ionospheric irregularities are detected in ten out of 12 strong storms. For the two special storms on 19 April 2002 (dDst min occurred at 21:00 LT) and 23 May 2002 (dDst min occurred at 20:00 LT), they did not trigger the generation of ionospheric irregularities although their dDst min occurred in 18:00–21:00 LT. Based on vertical total electron content (VTEC) derived from global positioning system (GPS) measurements, it is found that the westward electric fields during two storms should play a vital role to inhibit the nighttime ionospheric irregularities (NIIs) occurrence. This study suggests that caution should be taken when the dDst min determined LT is used to decide the occurrence of nighttime irregularities.

Ionospheric scintillation affects the tracking performance of GNSS code delay lock loop (DLL) and phase lock loop (PLL), thus degrading GNSS precise positioning accuracy. To investigate the BDS-3 signal tracking error characteristic under ionospheric scintillation, this study comprehensively analyzes the standard deviation of tracking error of DLL (σDLL) and PLL (σPLL) for BDS-3 B1I signal by using the data (from July 15, 2021 to August 9, 2022) collected by a newly ionospheric scintillation monitoring receiver (ISMR) station installed with the Septentrio PolaRx5S GNSS receiver at Le Dong of China (18.4°N, 108.9°E; geomagnetic latitude: 11.6°N). The analysis suggests that under scintillation activity, the σDLLand σPLLpoints of BDS-3 B1I show more stable and concentrated distribution than these of GPS L1C/A, GLONASS G1C/A, and Galileo E1C. Statistics indicate that the average values of σDLLfor BDS-3 B1I, GPS L1C/A, GLONASS G1C/A, and Galileo E1C are 0.089 m, 0.117 m, 0.100 m, and 0.126 m, and these of σPLLare 0.563 mm, 0.732 mm, 0.623 mm, and 0.789 mm, respectively. Furthermore, based on σDLLand σPLLof BDS-3 B1I, the receiver tracking error stochastic (RTES) model for BDS-3 B1I precise point positioning (PPP) is established in this study. The root-mean-square (RMS) statistics show that compared with the elevation angle stochastic (EAS) model, the RTES model can improve BDS-3 single-frequency PPP by approximately 32.0%, 23.5%, and 24.3% in September-October of 2021 and by 33.2%, 31.8%, and 31.8% in March-April of 2022 in the east, north, and up directions, respectively.

The 3-D computerized ionospheric tomography (CIT) based on Global Navigation Satellite System (GNSS) data is a classic ill-posed inverse problem. This study proposes an algorithm based on the semi-parametric model, which leverages the nonparametric component in the semi-parametric model to address systematic errors in CIT, thus improving the accuracy and effectiveness of reconstructed ionospheric electron density (IED). The feasibility and effectiveness of the proposed algorithm in processing systematic errors and reconstructing IED values are validated through simulation and real data experiments. In the simulation experiment, the proposed algorithm can separate systematic errors effectively. Compared with the Tikhonov regularization algorithm, the proposed algorithm offers improvements of 50.2% and 50.3% in root mean square error (RMSE) and the mean absolute error ($\Delta E$) for the reconstructed IEDs. The reconstructed 3-D ionospheric structure based on real data is consistent with the real spatiotemporal variation characteristics of the ionosphere. The average RMSE and$\Delta E$of the reconstructed slant total electron content (STEC) using the proposed algorithm are 30.2% and 32.1% higher than those of the Tikhonov regularization algorithm, respectively. The proposed algorithm demonstrates superior reconstruction performance in several aspects.

MOTIVATION: Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. RESULTS: In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. AVAILABILITYAND IMPLEMENTATION: The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Objective: The aim of our study was to explore the impact of maternal vasodilatationon on peripheral arterial tonometry used for assessing the endothelial function as physiological adaptation to pregnancy progress. Methods: Thirty-one healthy pregnant women of gestational weeks from 13 to 34 were tested using sphygmomanometer, peripheral arterial tonometry and photoplethysmographic assessment. Blood pressure, heart rate, reactive hyperemia index, augmentation index and reflection index were obtained in resting conditions. Data was analyzed in two groups divided as 2ndand 3rdtrimester and further divided into three groups of early 2ndtrimester, late 2ndtrimester and 3rdtrimester. Results: A rise of reactive hyperemia index of peripheral arterial tonometry from early 2ndto late 2ndtrimester and then a fall to 3rdtrimester had been found in association with continuous decrease of augmentation index derived either from peripheral arterial tonometry or photoplethysmography as well as reflection index from photoplethysmography and continuous increase of heart rate, during which blood pressure remained almost unchanged. A moderate level of correlation was observed between augmentation index derived from peripheral arterial tonometry and photoplethysmography. Conclusions: The reactive hyperemia index of peripheral arterial tonometry method is vasodilation dependent and likely to be less reliable in testing of endothelial function when more vasodilated.

MOTIVATION: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. RESULTS: To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. AVAILABILITY AND IMPLEMENTATION: https://github.com/lifanchen-simm/transformerCPI.

MOTIVATION: The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. RESULTS: KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. AVAILABILITY AND IMPLEMENTATION: KinomeX is available at: https://kinome.dddc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cognitive load assessment is crucial for user studies and human--computer interaction designs. As a noninvasive and easy-to-use category of measures, current photoplethysmogram- (PPG) based assessment methods rely on single or small-scale predefined features to recognize responses induced by people’s cognitive load, which are not stable in assessment accuracy. In this study, we propose a machine-learning method by using 46 kinds of PPG features together to improve the measurement accuracy for cognitive load. We test the method on 16 participants through the classical n-back tasks (0-back, 1-back, and 2-back). The accuracy of the machine-learning method in differentiating different levels of cognitive loads induced by task difficulties can reach 100% in 0-back vs. 2-back tasks, which outperformed the traditional HRV-based and single-PPG-feature-based methods by 12--55%. When using “leave-one-participant-out” subject-independent cross validation, 87.5% binary classification accuracy was reached, which is at the state-of-the-art level. The proposed method can also support real-time cognitive load assessment by beat-to-beat classifications with better performance than the traditional single-feature-based real-time evaluation method.

Measuring cognitive load and stress is crucial for ubiquitous human--computer interaction applications to dynamically understand and respond to the mental status of users, such as in smart healthcare, smart driving, and robotics. Various quantitative methods have been employed for this purpose, such as physiological and behavioral methods. However, the sensitivity, reliability, and usability are not satisfactory in many of the current methods, so they are not ideal for ubiquitous applications. In this study, we employed a reflected photoplethysmogram-based stress-induced vascular response index, i.e., the reflected sVRI (sVRI-r), to non-invasively measure the cognitive load and stress. This method has high usability as well as good sensitivity and reliability compared with the previously proposed transmitted sVRI (sVRI-t). We developed the basic methodology and detailed algorithm framework to validate the sVRI-r measurements, and it was implemented by employing two light sources, i.e., infrared light and green light. Compared with the simultaneously recorded blood pressure, heart rate variation, and sVRI-t, our findings demonstrated the greater potential of the sVRI-r for use as a sensitive, reliable, and usable parameter, as well as suggesting its potential integration with ubiquitous touch interactions for dynamic cognition and stress-sensing scenarios.

Objectives: Being the noninvasive measures of arterial stiffness, both brachial-ankle Pulse Wave Velocity (baPWV) and Stiffness Index (SI) had been used for more than ten years and broadly applied in clinical settings. However, the relationship of the two arterial stiffness measures detected at different sites but defined alike has not been fully investigated into the causes and circumstances. The aim of the present study is to assessment the relationship between baPWV and SI applied to evaluate hypertriglyceridemia (hyperTG) effects on arterial stiffness in clinical settings. Methods: A total of 61 (39 males and 22 females) subjects were subjected to biochemistry parameters measurements. Two kinds of parameters - baPWV and SI were used to evaluate the arterial stiffness. Here, in order to minimize the uncertainty in a comparative study of SI and baPWV, photoplethysmography (PPG) wave train was employed to accurately acquire the most representative pulses for calculation of SI value. Results: BaPWV was significantly correlated with SI (r = 0.60, p <; 0.01). The arterial stiffness of hyperTG group has a significance difference compared to normal control group. Conclusion: Our findings suggest both SI and baPWV could be a reliable estimator of arterial stiffness. There exists a significance difference of arterial stiffness among hyperTG group and normal control group. And the use of statistical method could be beneficial in improving effectively the measurement of arterial stiffness.

Objective: Ascent to high altitudes requires adaptation to a hypoxic and hypobaric environment. Induced altitude acclimatization may decrease susceptibility to acute mountain sickness (AMS). We aimed to exam the effects of acclimatization at 1520m on susceptibility to AMS during a subsequent rapid ascent to 3658m. Methods: Rate pressure product (RPP), oxygen saturation (SpO2) and vascular tone, quantified by the reflection index (RI) obtained using photoplethysmography (PPG) technique, were studied in fifty-five participants ascending to 3658m from 300 and 1520m defined as Group A and B respectively. AMS occurrence was evaluated by the Lake Louise Score (LLS) system. Results: Seventeen of the fifty-five participants were diagnosed with AMS. The incidence and severity were lower in Group B than Group A. On initial exposure, we observed a significant increase of RPP and a significant decrease of SpO2. Inside each group, either A or B, RI exhibited a quick and dramatic fall followed by an early recovery back to normal in subjects without AMS but a blunted and slow fall followed by a delayed recovery in subjects with AMS. A moderate level of inverse correlation was found between degree of fall (Δ) in SpO2 and RI within 24 hours. Conclusions: The induced altitude acclimatization provided low-altitude residents in certain degree benefit in prevention of AMS during a subsequent rapid ascent to high altitude. The comparison of data between participants with and without an induced altitude acclimatization exhibited physiological significance during acute phase response after a rapid ascend to altitude.

Quantitative assessment for cognitive load and mental stress is very important in optimizing human-computer system designs to improve performance and efficiency. Traditional physiological measures, such as heart rate variation (HRV), blood pressure and electrodermal activity (EDA), are widely used but still have limitations in sensitivity, reliability and usability. In this study, we propose a novel photoplethysmogram-based stress induced vascular index (sVRI) to measure cognitive load and stress. We also provide the basic methodology and detailed algorithm framework. We employed a classic experiment with three levels of task difficulty and three stages of testing period to verify the new measure. Compared with the blood pressure, heart rate and HRV components recorded simultaneously, the sVRI reached the same level of significance on the effect of task difficulty/period as the most significant other measure. Our findings showed sVRI's potential as a sensitive, reliable and usable parameter.

MOTIVATION: Discovering the relevant therapeutic targets for drug-like molecules, or their unintended 'off-targets' that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. RESULTS: As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand-target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. AVAILABILITY AND IMPLEMENTATION: TarPred is available at: http://www.dddc.ac.cn/tarpred.

MOTIVATION: The human uridine diphosphate-glucuronosyltransferase enzyme family catalyzes the glucuronidation of the glycosyl group of a nucleotide sugar to an acceptor compound (substrate), which is the most common conjugation pathway that serves to protect the organism from the potential toxicity of xenobiotics. Moreover, it could affect the pharmacological profile of a drug. Therefore, it is important to identify the metabolically labile sites for glucuronidation. RESULTS: In the present study, we developed four in silico models to predict sites of glucuronidation, for four major sites of metabolism functional groups, i.e. aliphatic hydroxyl, aromatic hydroxyl, carboxylic acid or amino nitrogen, respectively. According to the mechanism of glucuronidation, a series of 'local' and 'global' molecular descriptors characterizing the atomic reactivity, bonding strength and physical-chemical properties were calculated and selected with a genetic algorithm-based feature selection approach. The constructed support vector machine classification models show good prediction performance, with the balanced accuracy ranging from 0.88 to 0.96 on test set. For further validation, our models can successfully identify 84% of experimentally observed sites of metabolisms for an external test set containing 54 molecules. AVAILABILITY AND IMPLEMENTATION: The software somugt based on our models is available at www.dddc.ac.cn/adme/jlpeng/somugt_win32.zip.

MOTIVATION: One goal of metabolomics is to define and monitor the entire metabolite complement of a cell, while it is still far from reach since systematic and rapid approaches for determining the biotransformations of newly discovered metabolites are lacking. For drug development, such metabolic biotransformation of a new chemical entity (NCE) is of more interest because it may profoundly affect its bioavailability, activity and toxicity profile. The use of in silico methods to predict the site of metabolism (SOM) in phase I cytochromes P450-mediated reactions is usually a starting point of metabolic pathway studies, which may also assist in the process of drug/lead optimization. RESULTS: This article reports the Cytochromes P450 (CYP450)-mediated SOM prediction for the six most important metabolic reactions by incorporating the use of machine learning and semi-empirical quantum chemical calculations. Non-local models were developed on the basis of a large dataset comprising 1858 metabolic reactions extracted from 1034 heterogeneous chemicals. For validation, the overall accuracies of all six reaction types are higher than 0.81, four of which exceed 0.90. In further receiver operating characteristic (ROC) analyses, each of the SOM model gave a significant area under curve (AUC) value over 0.86, indicating a good predicting power. An external test was made on a previously published dataset, of which 80% of the experimentally observed SOMs can be correctly identified by applying the full set of our SOM models. AVAILABILITY: The program package SOME_v1.0 (Site Of Metabolism Estimator) developed based on our models is available at http://www.dddc.ac.cn/adme/myzheng/SOME_1_0.tar.gz.

BACKGROUND: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. DESCRIPTION: PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. CONCLUSION: PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

MOTIVATION: Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction. RESULTS: Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis. AVAILABILITY: The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.