Influence relevance voting: an accurate and interpretable virtual high throughput screening method.

Virtual screening (VS) methods have been shown to increase success rates in many drug discovery campaigns, when they complement experimental approaches, such as high-throughput screening methods or classical medicinal chemistry approaches. Nevertheless, predictive capability of VS is not yet optimal, mainly due to limitations in the underlying physical principles describing drug binding phenomena. One approach that can improve VS methods is the aid of machine learning methods. When enough experimental data are available to train such methods, predictive capability can considerably increase. We show in this research work how a multi-objective evolutionary search strategy for feature selection, which can provide with small and accurate decision trees that can be very easily understood by chemists, can drastically increase the applicability and predictive ability of these techniques and therefore aid considerable in the drug discovery problem. With the proposed methodology, we find classification models with accuracy between 0.9934 and 1.00 and area under ROC between 0.96 and 1.00 evaluated in full training sets, and accuracy between 0.9849 and 0.9940 and area under ROC between 0.89 and 0.93 evaluated with tenfold cross-validation over 30 iterations, while substantially reducing the model size.

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

To address the imbalanced data problem in molecular docking-based virtual screening methods, this paper proposes a virtual screening method for drug proteins based on imbalanced data mining, which introduces machine learning technology into the virtual screening technology for drug proteins to deal with the imbalanced data problem in the virtual screening process and improve the accuracy of the virtual screening. First, to address the data imbalance problem caused by the large difference between the number of active compounds and the number of inactive compounds in the docking conformation generated by the actual virtual screening process, this paper proposes a way to improve the data imbalance problem using SMOTE combined with genetic algorithm to synthesize new active compounds artificially by upsampling active compounds. Then, in order to improve the accuracy in the virtual screening process of drug proteins, the idea of integrated learning is introduced, and the random forest (RF) extended from Bagging integrated learning technique is combined with the support vector machine (SVM) technique, and the virtual screening of molecular docking conformations using RF-SVM technique is proposed to improve the prediction accuracy of active compounds in docking conformations. To verify the effectiveness of the proposed technique, first, HIV-1 protease and SRC kinase were used as test data for the experiments, and then, CA II was used to validate the model of the test data. The virtual screening of drug proteins using the proposed method in this paper showed an improvement in both enrichment factor (EF) and AUC compared with the use of the traditional virtual screening, for the test dataset. Therefore, it can be shown that the proposed method can effectively improve the accuracy of drug virtual screening.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

When dealing with large data sets, the traditional support vector machine (SVM) needs long training time which is aroused by the complexity of computation for kernel function. Moreover, if there are noises in a given training set, the classification accuracy rate of the traditional SVM is usually low. To overcome the shortcomings above, the algorithm of SVM based on half-suppressed fuzzy c-means clustering (HSFCM) is proposed. There are two phases in the proposed algorithm. First, the samples in each of the two classes are clustered by HSFCM. Second, the traditional SVM is trained only by the cluster centers obtained in the first phase. Experimental results show that the proposed method can reduce the number of training samples, enhance the training speed and classification accuracy rate of the traditional SVM effectively.

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, and . Similar metrics, calculated on an external set of data (e.g., ), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., and and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable and/or values were unable to pick a single active compound from within the pool whereas in other cases, models with poor and/or values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

We have developed a support vector machine (SVM) method that relies on core-measured data as well as gamma-ray, deep resistivity, sonic, and density wireline well-log data in identifying thermally mature total organic carbon (TOC)-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay Shale Formation data. The SVM method successfully classifies the TOC data set into TOC-rich and TOC-poor classes and the [Formula: see text] data set into thermally mature and thermally immature classes when the optimal features are selected. To further test the SVM approach, we generate depth-separated training and test data sets from a well in the Duvernay Shale Formation and successfully use the approach to identify thermally mature TOC-rich intervals. We also examine the successful cross basin application of the SVM approach in predicting TOC using data from the Barnett and Duvernay Shale Formations as the training and test data sets, respectively.

The holistic integration of virtual screening in drug discovery

Introduction Prepilin peptidases (PPP) are essential enzymes for the biogenesis of important virulence factors, such as type IV pili (T4P), type II secretion systems, and other T4P-related systems of bacteria and archaea. PPP inhibitors could be valuable pharmaceuticals, but only a few have been reported. Interestingly, PPP share similarities with presenilin enzymes from the gamma-secretase protease complex, which are linked to Alzheimer’s disease. Numerous gamma-secretase inhibitors have been reported, and some have entered clinical trials, but none has been tested against PPP. Objective The objective of this study is to develop a high-throughput screening (HTS) method to search for inhibitors of PPP from various chemical libraries and reported gamma-secretase inhibitors. Method More than 15,000 diverse compounds, including 13 reported gamma-secretase inhibitors and other reported peptidase inhibitors, were screened to identify potential PPP inhibitors. Results The authors developed a novel screening method and screened 15,869 compounds. However, the screening did not identify a PPP inhibitor. Nevertheless, the study suggests that gamma-secretase is sufficiently different from PPP that specific inhibitors may exist in a larger chemical space. Conclusion The authors believe that the HTS method that they describe has numerous advantages and encourage others to consider its application in the search for PPP inhibitors.

This paper addresses an important problem of training set selection for support vector machines (SVMs). It is a critical step in case of large and noisy data sets due to high time and memory complexity of the SVM training. There have been several methods proposed so far, in majority underpinned with the analysis of data geometry either in the input or kernel space. Here, we propose a new dynamically adaptive genetic algorithm (DAGA) to select valuable training sets. We demonstrate that not only can DAGA quickly select the training data, but in addition it dynamically determines the desired training set size without any prior information. We analyze the impact of the support vectors ratio, defined as the percentage of support vectors in the training set, on the DAGA performance. Also, we investigate and discuss the possibility of incorporating reduced SVMs into the proposed algorithm. Extensive experimental study shows that DAGA offers fast and effective training set optimization that is independent on the entire training set size.

Chapter 7 - Virtual screening of phytochemicals for drug discovery

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

The specifications of the forests in Kalimantan are part of the vegetation type which is characteristic of dry lowland forests that are rich in biodiversity. The Kalimantan forest area is known as an area that has the highest vascular biodiversity in the world. Until 2014, the rate of forest loss as a source of biodiversity was always high every year. The National Polar-Orbiting Suomi Partnership (Suomi NPP) is a new generation of weather satellite imagery from NASA which currently continues to develop application algorithms for environmental monitoring. The support vector machine (SVM) method is a one-class learning machine method with artificial neural network methods that can recognize patterns from input or examples given and also include supervised learning. This study aims to analyze the effect of each parameter on the SVM method and the combination that can produce the highest accuracy, and analyze the ability of the SVM method for making hotspot maps based on the number of forest fire areas in East Kalimantan. The results of the study showed that the Support Vector Machine (SVM) method on the spectral data produced a total of 85%.