Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Nischal Karki; Niraj Verma; Francesco Trozzi; Peng Tao; Elfi Kraka; Brian Zoltowski

doi:10.3390/ijms22041573

Recent Drug-Repurposing-Driven Advances in the Discovery of Novel Antibiotics

Current Medicinal Chemistry ◽

10.2174/0929867325666180706101404 ◽

2019 ◽

Vol 26 (28) ◽

pp. 5363-5388 ◽

Cited By ~ 8

Author(s):

Ananda Kumar Konreddy ◽

Grandhe Usha Rani ◽

Kyeong Lee ◽

Yongseok Choi

Keyword(s):

Drug Discovery ◽

Bacterial Infections ◽

De Novo ◽

Genetic Disorder ◽

Antibacterial Properties ◽

Drug Repurposing ◽

Global Public Health ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Novel Antibiotics

: Drug repurposing is a safe and successful pathway to speed up the novel drug discovery and development processes compared with de novo drug discovery approaches. Drug repurposing uses FDA-approved drugs and drugs that failed in clinical trials, which have detailed information on potential toxicity, formulation, and pharmacology. Technical advancements in the informatics, genomics, and biological sciences account for the major success of drug repurposing in identifying secondary indications of existing drugs. Drug repurposing is playing a vital role in filling the gap in the discovery of potential antibiotics. Bacterial infections emerged as an ever-increasing global public health threat by dint of multidrug resistance to existing drugs. This raises the urgent need of development of new antibiotics that can effectively fight multidrug-resistant bacterial infections (MDRBIs). The present review describes the key role of drug repurposing in the development of antibiotics during 2016–2017 and of the details of recently FDA-approved antibiotics, pipeline antibiotics, and antibacterial properties of various FDA-approved drugs of anti-cancer, anti-fungal, anti-hyperlipidemia, antiinflammatory, anti-malarial, anti-parasitic, anti-viral, genetic disorder, immune modulator, etc. Further, in view of combination therapies with the existing antibiotics, their potential for new implications for MDRBIs is discussed. The current review may provide essential data for the development of quick, safe, effective, and novel antibiotics for current needs and suggest acuity in its effective implications for inhibiting MDRBIs by repurposing existing drugs.

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Gliptin Repurposing for COVID-19

10.26434/chemrxiv.12110760 ◽

2020 ◽

Author(s):

Matthew Groves ◽

Alexander Domling ◽

Angel Jonathan Ruiz Moreno ◽

Atilio Reyes Romero ◽

Constantinos Neochoritis ◽

...

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Drug Class ◽

De Novo ◽

Drug Repurposing ◽

Therapeutic Modality ◽

Antidiabetic Drug ◽

Short Term ◽

Computational Screening ◽

Approved Drugs

<i>De novo</i> drug discovery of any therapeutic modality (e.g. antibodies, vaccines or small molecules) historically takes years from idea/preclinic to the market and it is therefore not a short-term solution for the current SARS-CoV-2 pandemic. Therefore, drug repurposing – the discovery novel indication areas for already approved drugs - is perhaps the only approach able to yield a short term relieve. Here we describe computational screening results suggesting that certain members of the drug class of gliptins are inhibitors of the two SARS-CoV-2 proteases 3CLpro and PLpro. The oral bioavailable antidiabetic drug class of gliptins are safe and have been introduced clinically since 2006 and used by millions of patients since then. Based on our repurposing hypothesis the nitrile containing gliptins deserve further investigation as potential anti-COVID19 drugs.

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Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Big Data and Cognitive Computing ◽

10.3390/bdcc5040075 ◽

2021 ◽

Vol 5 (4) ◽

pp. 75

Author(s):

Aulia Fadli ◽

Wisnu Ananta Kusuma ◽

Annisa ◽

Irmanida Batubara ◽

Rudi Heryanto

Keyword(s):

Drug Discovery ◽

Drug Target ◽

Deep Neural Network ◽

Drug Repurposing ◽

Discovery Process ◽

Drug Discovery Process ◽

Herbal Plants ◽

Approved Drugs ◽

Pubchem Fingerprint ◽

F Measure

Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

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Gliptin Repurposing for COVID-19

10.26434/chemrxiv.12110760.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Matthew Groves ◽

Alexander Domling ◽

Angel Jonathan Ruiz Moreno ◽

Atilio Reyes Romero ◽

Constantinos Neochoritis ◽

...

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Drug Class ◽

De Novo ◽

Drug Repurposing ◽

Therapeutic Modality ◽

Antidiabetic Drug ◽

Short Term ◽

Computational Screening ◽

Approved Drugs

<i>De novo</i> drug discovery of any therapeutic modality (e.g. antibodies, vaccines or small molecules) historically takes years from idea/preclinic to the market and it is therefore not a short-term solution for the current SARS-CoV-2 pandemic. Therefore, drug repurposing – the discovery novel indication areas for already approved drugs - is perhaps the only approach able to yield a short term relieve. Here we describe computational screening results suggesting that certain members of the drug class of gliptins are inhibitors of the two SARS-CoV-2 proteases 3CLpro and PLpro. The oral bioavailable antidiabetic drug class of gliptins are safe and have been introduced clinically since 2006 and used by millions of patients since then. Based on our repurposing hypothesis the nitrile containing gliptins deserve further investigation as potential anti-COVID19 drugs.

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Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

10.26434/chemrxiv.12362213.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nischal Komal Karki ◽

Niraj Verma ◽

Francesco Trozzi ◽

Peng Tao ◽

Elfi Kraka ◽

...

Keyword(s):

Machine Learning ◽

Open Access ◽

Viral Entry ◽

De Novo ◽

Global Scale ◽

Pharmaceutical Products ◽

Potential Efficacy ◽

Docking Approach ◽

Approved Drugs ◽

New Treatments

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. Currently, research labs around the world are looking for new pharmaceutical treatments by repurposing existing drugs, identifying potential antibody-based therapeutics, as well as the design of new pharmaceutical products and vaccines. To be able to rapidly identify potentional new treatments we require global cooperation and an enhanced open-access research model to distribute new ideas and leads. Herein, we employ a combined machine learning and drug docking approach to evaluate the potential efficacy of existing FDA and World approved drugs to impact the ACE2-Spike complex necessary for viral entry and replication. Further, we extend the machine learning approach to databases containing between 700,000-1 billion compounds. The results of large library screens are incorporated into a open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed, and de novo design of ACE2-regulatory compounds. Through these efforts we identify intriguing links between COVID-19 pathologies, particularly in regard to possible sex-differences in disease outcomes.

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Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

10.26434/chemrxiv.12362213 ◽

2020 ◽

Author(s):

Nischal Komal Karki ◽

Niraj Verma ◽

Francesco Trozzi ◽

Peng Tao ◽

Elfi Kraka ◽

...

Keyword(s):

Machine Learning ◽

Open Access ◽

Viral Entry ◽

De Novo ◽

Global Scale ◽

Pharmaceutical Products ◽

Potential Efficacy ◽

Docking Approach ◽

Approved Drugs ◽

New Treatments

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. Currently, research labs around the world are looking for new pharmaceutical treatments by repurposing existing drugs, identifying potential antibody-based therapeutics, as well as the design of new pharmaceutical products and vaccines. To be able to rapidly identify potentional new treatments we require global cooperation and an enhanced open-access research model to distribute new ideas and leads. Herein, we employ a combined machine learning and drug docking approach to evaluate the potential efficacy of existing FDA and World approved drugs to impact the ACE2-Spike complex necessary for viral entry and replication. Further, we extend the machine learning approach to databases containing between 700,000-1 billion compounds. The results of large library screens are incorporated into a open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed, and de novo design of ACE2-regulatory compounds. Through these efforts we identify intriguing links between COVID-19 pathologies, particularly in regard to possible sex-differences in disease outcomes.

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Recent Advances in Drug Repurposing for Parkinson’s Disease

Current Medicinal Chemistry ◽

10.2174/0929867325666180719144850 ◽

2019 ◽

Vol 26 (28) ◽

pp. 5340-5362 ◽

Cited By ~ 2

Author(s):

Xin Chen ◽

Giuseppe Gumina ◽

Kristopher G. Virga

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Drug Discovery ◽

De Novo ◽

Drug Repositioning ◽

Drug Repurposing ◽

Cost Effective ◽

New Drugs ◽

Recent Advances ◽

Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

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Optimising Performance for NB-IoT UE Devices through Data Driven Models

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010021 ◽

2021 ◽

Vol 10 (1) ◽

pp. 21

Author(s):

Omar Nassef ◽

Toktam Mahmoodi ◽

Foivos Michelinakis ◽

Kashif Mahmood ◽

Ahmed Elmokashfi

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Gradient Descent ◽

Deep Neural Network ◽

Narrow Band ◽

Learning Algorithm ◽

Base Station ◽

User Equipment ◽

Data Driven ◽

Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽

10.3390/e22090949 ◽

2020 ◽

Vol 22 (9) ◽

pp. 949

Author(s):

Jiangyi Wang ◽

Min Liu ◽

Xinwu Zeng ◽

Xiaoqiang Hua

Keyword(s):

Neural Network ◽

Signal Detection ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection Method ◽

Learning Algorithm ◽

Simulated Data ◽

Data Sets ◽

Feature Maps ◽

Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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