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

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

10.26434/chemrxiv.13052951.v2 ◽

2021 ◽

Author(s):

Ben Geoffrey A S ◽

Rafal Madaj ◽

Akhil Sanker ◽

Pavan Preetham Valluri ◽

Harshmeet Singh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

In Silico ◽

Drug Target ◽

Learning Algorithms ◽

Network Data ◽

Ligand Interaction ◽

Data Set ◽

Target Interaction ◽

Pubchem Compound

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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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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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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Machine Learning Methods in Drug Discovery

Molecules ◽

10.3390/molecules25225277 ◽

2020 ◽

Vol 25 (22) ◽

pp. 5277

Author(s):

Lauv Patel ◽

Tripti Shukla ◽

Xiuzhen Huang ◽

David W. Ussery ◽

Shanzhi Wang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Discovery ◽

High Throughput Screening ◽

Drug Targets ◽

Learning Algorithms ◽

Machine Learning Techniques ◽

Online Information ◽

Drug Candidates ◽

Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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Predicting drug-target interaction network using deep learning model

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2019.03.016 ◽

2019 ◽

Vol 80 ◽

pp. 90-101 ◽

Cited By ~ 18

Author(s):

Jiaying You ◽

Robert D. McLeod ◽

Pingzhao Hu

Keyword(s):

Deep Learning ◽

Drug Target ◽

Interaction Network ◽

Learning Model ◽

Target Interaction ◽

Deep Learning Model

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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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