Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Lusci, Alessandro; Fooshee, David; Browning, Michael R.; Swamidass, S. Joshua; Baldi, Pierre

doi:10.1186/s13321-015-0110-6

Cited by 27 publications

(31 citation statements)

References 50 publications

(47 reference statements)

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“…A later study by Lusci et al . that was performed on ChEMBL data release 13 benchmarked the performance of a number of different algorithms [33]. Similar to the current work the authors performed a temporal validation (on ChEMBL release 13) as a more realistic estimate of model performance.…”

Section: Resultsmentioning

confidence: 96%

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Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

Lenselink

Dijke

Bongers

et al. 2017

Preprint

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The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics.In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naive Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution.Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized DNN_PCM).Here, a standardized set to test and evaluate different machine learning algorithms in the context of multitask learning is offered by providing the data and the protocols.

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Section: Resultsmentioning

confidence: 96%

“…Typically, studies have used thresholds for activity between 5 and 6 [10, 11, 32, 33]. Data points here were assigned to the ‘active’ class if the pCHEMBL value was equal to or greater than 6.5 (corresponding to approximately 300 nM) and to the ‘inactive’ class if the pCHEMBL value was below 6.5.…”

Section: Methodsmentioning

confidence: 99%

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

Lenselink

Dijke

Bongers

et al. 2017

Preprint

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“…The aforementioned publicly available databases have been widely used in numerous cheminformatics studies [14–16]. However, the curated data are quite heterogeneous [17] and lack a standard way for annotating biological endpoints, mode of action and target identifier.…”

Section: Introductionmentioning

confidence: 99%

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

et al. 2017

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Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0203-5) contains supplementary material, which is available to authorized users.

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“…IRV influences are decomposed, also nonlinearly, into a relevance component and a vote component. Therefore, the predictions of the IRV is by nature transparent, as the exact data used to make a prediction can be extracted from the network by examining each prediction's influences, making it closer to a “white‐box” neural network method …”

Section: Reservations About Deep Learning and Of Being A Black Boxmentioning

confidence: 99%

Deep learning for computational chemistry

2017

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The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against nonneural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. 3 IntroductionDeep Learning is the key algorithm used in the development of AlphaGo, a Deep learning is a machine learning algorithm, not unlike those already in use in various applications in computational chemistry, from computer-aided drug design to materials property prediction. 5-8 Amongst some of its more high profile achievements include the Merck activity prediction challenge in 2012, where a deep neural network not only won the competition and outperformed Merck's internal baseline model, but did so without having a single chemist or biologist in their team. In a repeated success by a different research team, deep learning models achieved top positions in the Tox21 toxicity prediction challenge issued by NIH in 2014. 9 The unusually stellar performance of deep learning models in both predicting activity and toxicity in these recent examples, originate from the unique characteristics that distinguishes deep learning from traditional machine learning algorithms.For those unfamiliar with the intricacies of machine learning algorithms, we will highlight some of the key differences between traditional (shallow) machine learning and deep learning.The simplest example of a machine learning algorithm would be the ubiquitous least-squares linear regression. In linear regression, the underlying nature of the model is known (linear in th...

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Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Cited by 27 publications

References 50 publications

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Deep learning for computational chemistry

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