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

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

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

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>

scholarly journals Target2DeNovoDrug : a novel programmatic tool for deep learning based de novo drug design for a target of interest

2020 ◽  
Author(s):  
Rafal Madaj ◽  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
In Silico ◽  
High Performance ◽  
Data Science ◽  
De Novo ◽  
Computationally Efficient ◽  
De Novo Drug Design ◽  
Necrosis Factor Alpha ◽  
Structure Based Drug Design

The on-going data-science and AI revolution offers researchers with fresh set of tools to approach structure-based drug design problems in the computer aided drug design space. A novel programmatic tool that can be used in aid of in silico-deep learning based de novo drug design for any target of interest has been reported. Once the user specifies the target of interest, the programmatic workflow of the tool generates novel SMILES of compounds that are likely to be active against the target. The tool also performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results in the working folder of the user. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained and supported at the GitHub repository given in link below https://github.com/bengeof/Target2DeNovoDrug

scholarly journals A Structure-Based Drug Discovery Paradigm

2019 ◽  
Vol 20 (11) ◽  
pp. 2783 ◽  
Author(s):  
Maria Batool ◽  
Bilal Ahmad ◽  
Sangdun Choi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Learning Tools ◽  
Statistical Machine Learning ◽  
Lead Discovery ◽  
De Novo Drug Design ◽  
Structure Based Drug Design

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

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

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>

scholarly journals Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

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

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>

scholarly journals 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

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>

scholarly journals Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidates

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Mario Sergio Valdés Tresanco ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Drug Discovery ◽  
In Silico ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Ligand Interaction ◽  
Drug Candidates ◽  
Descriptor Selection ◽  
Drug Leads

<p>The work is composed of python based programmatic tool that automates the dry lab drug discovery workflow for coronavirus. Firstly, the python program is written to automate the process of data mining PubChem database to collect data required to perform a machine learning based AutoQSAR algorithm through which drug leads for coronavirus are generated. The data acquisition from PubChem was carried out through python web scrapping techniques. The workflow of the machine learning based AutoQSAR involves feature learning and descriptor selection, QSAR modelling, validation and prediction. The drug leads generated by the program are required to satisfy the Lipinski’s drug likeness criteria as compounds that satisfy Lipinski’s criteria are likely to be an orally active drug in humans. Drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package to computer model the interaction of the compounds generated as drug leads and the coronaviral drug target identified with their PDB ID : 6Y84. The results are stored in the working folder of the user. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Select drug leads were further studied extensively using Molecular Dynamics Simulations and best binders and their reactive profiles were analysed using Molecular Dynamics and Density Functional Theory calculations. Thus our programmatic tool ushers in a new age of automatic ease in drug identification for coronavirus. </p><p><br></p><p><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><br></p><p>https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</p>

scholarly journals Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidates

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Mario Sergio Valdés Tresanco ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Drug Discovery ◽  
In Silico ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Ligand Interaction ◽  
Drug Candidates ◽  
Descriptor Selection ◽  
Drug Leads

<p>The work is composed of python based programmatic tool that automates the dry lab drug discovery workflow for coronavirus. Firstly, the python program is written to automate the process of data mining PubChem database to collect data required to perform a machine learning based AutoQSAR algorithm through which drug leads for coronavirus are generated. The data acquisition from PubChem was carried out through python web scrapping techniques. The workflow of the machine learning based AutoQSAR involves feature learning and descriptor selection, QSAR modelling, validation and prediction. The drug leads generated by the program are required to satisfy the Lipinski’s drug likeness criteria as compounds that satisfy Lipinski’s criteria are likely to be an orally active drug in humans. Drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package to computer model the interaction of the compounds generated as drug leads and the coronaviral drug target identified with their PDB ID : 6Y84. The results are stored in the working folder of the user. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Select drug leads were further studied extensively using Molecular Dynamics Simulations and best binders and their reactive profiles were analysed using Molecular Dynamics and Density Functional Theory calculations. Thus our programmatic tool ushers in a new age of automatic ease in drug identification for coronavirus. </p><p><br></p><p><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><br></p><p>https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</p>

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