Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures

ABSTRACTThe characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers, thus shedding light on the de novo detection of nucleotide modifications.

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kGCN: a graph-based deep learning framework for chemical structures

Journal of Cheminformatics ◽

10.1186/s13321-020-00435-6 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Ryosuke Kojima ◽

Shoichi Ishida ◽

Masateru Ohta ◽

Hiroaki Iwata ◽

Teruki Honma ◽

...

Keyword(s):

Deep Learning ◽

Learning Framework ◽

Chemical Structures

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Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches

Briefings in Bioinformatics ◽

10.1093/bib/bbaa212 ◽

2020 ◽

Author(s):

Tianyi Zhao ◽

Yang Hu ◽

Liang Cheng

Keyword(s):

Deep Learning ◽

Chemical Structure ◽

Computational Method ◽

Learning Approaches ◽

Convolutional Network ◽

Functional Changes ◽

Chemical Structures ◽

Association Pattern ◽

Semantic Associations ◽

Components Analysis

Abstract Motivation: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. Results: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. Contact: [email protected]. Availability and implementation: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

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DeepSimulator: a deep simulator for Nanopore sequencing

10.1101/238683 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yu Li ◽

Renmin Han ◽

Chongwei Bi ◽

Mo Li ◽

Sheng Wang ◽

...

Keyword(s):

Deep Learning ◽

De Novo ◽

Real Data ◽

Electrical Current ◽

Complex Nature ◽

Main Task ◽

Nanopore Sequencing ◽

Base Calling ◽

Context Dependent ◽

Sequencing Procedure

ABSTRACTMotivationOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.ResultsHere we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.AvailabilityThe software can be accessed freely at: https://github.com/lykaust15/deep_simulator.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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kGCN: A Graph-Based Deep Learning Framework for Chemical Structures

10.26434/chemrxiv.11859684.v1 ◽

2020 ◽

Author(s):

Ryosuke Kojima ◽

Shoichi Ishida ◽

Masateru Ohta ◽

Hiroaki Iwata ◽

Teruki Honma ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Prediction Models ◽

Bayesian Optimization ◽

Factors Affecting ◽

Learning Framework ◽

Chemical Structures ◽

Programming Skills ◽

Model Tuning ◽

Interpretation Of Results

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multimodal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo/kGCN.</div>

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A Deep Learning Framework for Constitutive Modeling based on Temporal Convolutional Network

Journal of Computational Physics ◽

10.1016/j.jcp.2021.110784 ◽

2021 ◽

pp. 110784

Author(s):

Jia-Ji Wang ◽

Chen Wang ◽

Jian-Sheng Fan ◽

Y.L. Mo

Keyword(s):

Deep Learning ◽

Constitutive Modeling ◽

Convolutional Network ◽

Learning Framework

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A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

Neural Plasticity ◽

10.1155/2020/8863223 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Juntao Xue ◽

Feiyue Ren ◽

Xinlin Sun ◽

Miaomiao Yin ◽

Jialing Wu ◽

...

Keyword(s):

Deep Learning ◽

Motor Imagery ◽

Neural System ◽

Brain Network ◽

Eeg Signals ◽

Stroke Patients ◽

Common Spatial Pattern ◽

Convolutional Network ◽

The Public ◽

Learning Framework

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127.v1 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology

10.26434/chemrxiv.14474127.v2 ◽

2021 ◽

Author(s):

Xuhan Liu ◽

Kai Ye ◽

Herman Van Vlijmen ◽

Michael T. M. Emmerich ◽

Adriaan P. IJzerman ◽

...

Keyword(s):

Deep Learning ◽

Reinforcement Learning ◽

Drug Target ◽

De Novo ◽

Multi Objective ◽

Learning Framework ◽

Drug Molecules ◽

Pareto Ranking ◽

Speed Up ◽

Deep Learning Model

In polypharmacology, ideal drugs are required to bind to multiple specific targets to enhance efficacy or to reduce resistance formation. Although deep learning has achieved breakthrough in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules in spite of the reality that drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug molecules towards more than one specific target (two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment, both of which were pre-trained in advance and then interplayed under the reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used for constructing Pareto ranks of the generated molecules with non-dominated sorting and Tanimoto-based crowding distance algorithms. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate more desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity.

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