Motivation Drug discovery demands rapid quantification of compound–protein interaction (CPI). However, there is a lack of methods that can predict compound–protein affinity from sequences alone with high applicability, accuracy and interpretability. Results We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug–target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. Availability and implementation Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. Supplementary information Supplementary data are available at Bioinformatics online.

In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of ‘unit patch of residue triangle’ was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported.

No abstract

Global light transport is composed of direct and indirect components. In this paper, we take the first steps toward analyzing light transport using high temporal resolution information via time of flight (ToF) images. The time profile at each pixel encodes complex interactions between the incident light and the scene geometry with spatially-varying material properties. We exploit the time profile to decompose light transport into its constituent direct, subsurface scattering, and interreflection components.We show that the time profile is well modelled using a Gaussian function for the direct and interreflection components, and a decaying exponential function for the subsurface scattering component. We use our direct, subsurface scattering, and interreflection separation algorithm for four computer vision applications: recovering projective depth maps, identifying subsurface scattering objects, measuring parameters of analytical subsurface scattering models, and performing edge detection using ToF images.

In the drug discovery process, unstable compounds in storage can lead to false positive or false negative bioassay conclusions. Prediction of the chemical stability of a compound by de novo methods is complex. Chemical instability prediction is commonly based on a model derived from empirical data. The COMDECOM (COMpound DECOMposition) project provides the empirical data for prediction of chemical stability. Models such as the extended-connectivity fingerprint and atom center fragments were built from the COMDECOM data and used for estimation of chemical stability, but deficits in the existing models remain. In this paper, we report DeepChemStable, a model employing an attention-based graph convolution network based on the COMDECOM data. The main advantage of this method is that DeepChemStable is an end-to-end model, which does not predefine structural fingerprint features, but instead, dynamically learns structural features and associates the features through the learning process of an attention-based graph convolution network. The previous ChemStable program relied on a rule-based method to reduce the false negatives. DeepChemStable, on the other hand, reduces the risk of false negatives without using a rule-based method. Because minimizing the rate of false negatives is a greater concern for instability prediction, this feature is a major improvement. This model achieves an AUC value of 84.7%, recall rate of 79.8%, and 10-fold stratified cross-validation accuracy of 79.1%.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.

The sustained progress of VLSI technology has altered the landscape of routing which is a major physical design stage. For timing driven routings, traditional approaches which consider only wire self capacitance become inadequate since the wire delay is affected more by coupling capacitance in ultra-deep submicron designs. Furthermore, the technology scaling dramatically increases the likelihood of the antenna problem in manufacturing and requests corresponding considerations in the routing stage. In this paper, we propose techniques that can be applied to handle the coupling aware timing and the antenna problem simultaneously during layer assignment which is an important step between global routing and detailed routing. An improved probabilistic coupling capacitance model is suggested for coupling aware timing optimization without performing track assignment. The antenna avoidance problem is modeled as a tree partitioning problem with a linear time optimal algorithm solution. This algorithm is customized to guide antenna avoidance in layer assignment. A linear time optimal jumper insertion algorithm is also derived. Experimental results on benchmark circuits show that the proposed techniques can lead to an average of ¾ ¼Ô× timing slack improvement validated by track assignment, ± antenna violation reduction and ± via violation reduction.