Compound2DeNovoDrugPropMax –a novel programmatic tool incorporating deep learning and in silico methods for automated bio-activity discovery for any compound of interest

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository<div><br></div>https://github.com/bengeof/Compound2DeNovoDrugPropMax<br>

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QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

10.26434/chemrxiv.13052951.v3 ◽

2021 ◽

Author(s):

Ben Geoffrey A S ◽

Rafal Madaj ◽

Akhil Sanker ◽

Pavan Preetham Valluri

Keyword(s):

Machine Learning ◽

Deep Learning ◽

In Silico ◽

Drug Target ◽

Drug Targets ◽

Learning Algorithms ◽

Interaction Network ◽

Network Data ◽

Data Set ◽

Target Interaction

Network data is composed of nodes and edges. Successful application of machine learning/deep<br>learning algorithms on network data to make node classification and link prediction have been shown<br>in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on<br>biological network data to generate predictions of scientific usefulness. In the presented work,<br>compound-drug target interaction network data set from bindingDB has been used to train deep<br>learning neural network and a multi class classification has been implemented to classify PubChem<br>compound queried by the user into class labels of PBD IDs. This way target interaction prediction for<br>PubChem compounds is carried out using deep learning. The user is required to input the PubChem<br>Compound ID (CID) of the compound the user wishes to gain information about its predicted<br>biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for<br>the input CID. Further the tool also optimizes the compound of interest of the user toward drug<br>likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In<br>Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand<br>interaction profiles. The program is hosted, supported and maintained at the following GitHub<br><div>repository</div><div><br></div><div>https://github.com/bengeof/Compound2DeNovoDrugPropMax</div><div><br></div>Anticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use<br>the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep<br>learning models into a quantum layer and introduce quantum layers into classical models to produce a<br>quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the<br><div>same is provided below</div><div><br></div>https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax<br>

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Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

10.26434/chemrxiv.13052951 ◽

2020 ◽

Author(s):

Ben Geoffrey A S ◽

Pavan Preetham Valluri ◽

Akhil Sanker ◽

Rafal Madaj ◽

Host Antony Davidd ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Molecular Docking ◽

Drug Target ◽

Drug Targets ◽

Learning Algorithms ◽

Network Data ◽

Ligand Interaction ◽

Pubchem Compound ◽

Protein Ligand Interaction

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>

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Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

10.26434/chemrxiv.13052951.v1 ◽

2020 ◽

Author(s):

Ben Geoffrey A S ◽

Pavan Preetham Valluri ◽

Akhil Sanker ◽

Rafal Madaj ◽

Host Antony Davidd ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Molecular Docking ◽

Drug Target ◽

Drug Targets ◽

Learning Algorithms ◽

Network Data ◽

Ligand Interaction ◽

Pubchem Compound ◽

Protein Ligand Interaction

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QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

10.31219/osf.io/b8y79 ◽

2021 ◽

Author(s):

Ben Geoffrey ◽

Rafal Madaj ◽

Pavan Preetham Valluri ◽

Akhil Sanker

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Discovery ◽

In Silico ◽

Drug Target ◽

Data Science ◽

De Novo ◽

Autodock Vina ◽

De Novo Drug Design ◽

Ligand Interaction

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for aparticular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activityagainst the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of theinteraction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock- Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in thefollowing GitHub repositoryhttps://github.com/bengeof/Target2DeNovoDrugPropMaxAnticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into our classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided belowhttps://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax

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Comparison of Sampling Methods Using Machine Learning and Deep Learning Algorithms with an Imbalanced Data Set for the Prevention of Violence Against Physicians

10.1109/uyms54260.2021.9659758 ◽

2021 ◽

Author(s):

Hilal Cakir ◽

Nilgun Incereis ◽

Bekir Tevfik Akgun ◽

Av. S. Yazgulu Tastemir

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sampling Methods ◽

Learning Algorithms ◽

Imbalanced Data ◽

Data Set ◽

Prevention Of Violence

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A Program for Automated Data Mining of PubChem to Screen a Billion Compounds and Generate by Machine Learning Based AutoQSAR Algorithm Anti-Corona Viral Drug Leads (Replicase Polyprotein 1ab Inhibitors) and in Silico Study of the Top Drug Lead Compounds

10.26434/chemrxiv.12423638.v1 ◽

2020 ◽

Author(s):

Ben Geoffrey A S ◽

Akhil Sanker ◽

Host Antony Davidd ◽

Judith Gracia

Keyword(s):

Machine Learning ◽

Data Mining ◽

In Silico ◽

Drug Target ◽

Data Set ◽

Lead Compounds ◽

Pubchem Database ◽

Drug Lead ◽

Lead Generation ◽

Drug Leads

Our work is composed of a python program for automatic data mining of PubChem database to collect data associated with the corona virus drug target replicase polyprotein 1ab (UniProt identifier : POC6X7 ) of data set involving active compounds, their activity value (IC50) and their chemical/molecular descriptors to run a machine learning based AutoQSAR algorithm on the data set to generate anti-corona viral drug leads. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic anti-corona viral drug lead generation reflective of the constantly growing PubChem database. The program prints out the top anti-corona viral drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and two corona viral drug target proteins, 3-Cystiene like Protease (3CLPro) and Papain like protease (PLpro) was studied and analysed using molecular docking tools. The compounds generated as drug leads by the program showed favourable interaction with the drug target proteins and thus we recommend the program for use in anti-corona viral compound drug lead generation as it helps reduce the complexity of virtual screening and ushers in an age of automatic ease in drug lead generation. The leads generated by the program can further be tested for drug potential through further In Silico, In Vitro and In Vivo testing <div><br></div><div><div>The program is hosted, maintained and supported at the GitHub repository link given below</div><div><br></div><div>https://github.com/bengeof/Drug-Discovery-P0C6X7</div></div><div><br></div>

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Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha48412041 ◽

2020 ◽

Vol 48 (4) ◽

pp. 2316-2327

Author(s):

Caner KOC ◽

Dilara GERDAN ◽

Maksut B. EMİNOĞLU ◽

Uğur YEGÜL ◽

Bulent KOC ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ensemble Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Performance Criteria ◽

Gradient Boosting ◽

Data Set

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

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Comparative Studies On Drug-Target Interaction Prediction Using Machine Learning and Deep Learning Methods With Different Molecular Descriptors

2021 International Conference of Women in Data Science at Taif University (WiDSTaif ) ◽

10.1109/widstaif52235.2021.9430198 ◽

2021 ◽

Author(s):

Haswani Ismail ◽

Nurul Hashimah Ahamed Hassain Malim ◽

Siti Zuraidah Mohamad Zobir ◽

Habibah A. Wahab

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Comparative Studies ◽

Drug Target ◽

Molecular Descriptors ◽

Learning Methods ◽

Target Interaction ◽

Interaction Prediction

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Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-In silico Approach Combining In vitro Experiments and Machine Learning

Frontiers in Immunology ◽

10.3389/fimmu.2018.00393 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 3

Author(s):

Elisabeth Zechendorf ◽

Phillip Vaßen ◽

Jieyi Zhang ◽

Ahmed Hallawa ◽

Antons Martincuks ◽

...

Keyword(s):

Machine Learning ◽

Cell Culture ◽

Heparan Sulfate ◽

In Silico ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Receptor Interaction ◽

Small Data ◽

Data Set

Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that HS induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In silico approach that combines conventional cell culture experiments with machine learning algorithms, we aimed to reduce a significant part of the expensive and time-consuming cell culture experiments and data generation by using computational intelligence (refinement and replacement). Cardiomyocytes exposed to HS showed an activation of the intrinsic apoptosis signal pathway via cytochrome C and the activation of caspase 3 (both p < 0.001). Notably, the exposure of HS resulted in the induction of necroptosis by tumor necrosis factor α and receptor interaction protein 3 (p < 0.05; p < 0.01) and, hence, an increased level of necrotic cardiomyocytes. In conclusion, using this novel Medical-In silico approach, our data suggest (i) that HS induces necroptosis in cardiomyocytes by phosphorylation (activation) of receptor-interacting protein 3, (ii) that HS is a therapeutic target in trauma- or sepsis-associated cardiomyopathy, and (iii) indicate that this proof-of-concept is a first step toward simulating the extent of activated components in the pro-apoptotic pathway induced by HS with only a small data set gained from the in vitro experiments by using machine learning algorithms.

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A Research on Deep Learning Advance for Landslide Classification using Convolutional Neural Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1184.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 903-906

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Proposed Model

Landslides can easily be tragic to human life and property. Increase in the rate of human settlement in the mountains has resulted in safety concerns. Landslides have caused economic loss between 1-2% of the GDP in many developing countries. In this study, we discuss a deep learning approach to detect landslides. Convolutional Neural Networks are used for feature extraction for our proposed model. As there was no source of an exact and precise data set for feature extraction, therefore, a new data set was built for testing the model. We have tested and compared this work with our proposed model and with other machine-learning algorithms such as Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors and Support Vector Machine. Our proposed deep learning model produces a classification accuracy of 96.90% outperforming the classical machine-learning algorithms.

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