scholarly journals Intense bitterness of molecules: machine learning for expediting drug discovery

2020 ◽  
Author(s):  
Eitan Margulis ◽  
Ayana Dagan-Wiener ◽  
Robert S. Ives ◽  
Sara Jaffari ◽  
Karsten Siems ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Development ◽  
Financial Burden ◽  
Medication Regimen ◽  
Drug Candidates ◽  
Aversive Taste ◽  
Animal Use ◽  
Bitterness Masking ◽  
Late Stages

AbstractDrug development is a long, expensive and multistage process geared to achieving safe drugs with high efficacy. A crucial prerequisite for completing the medication regimen for oral drugs, particularly for pediatric and geriatric populations, is achieving taste that does not hinder compliance. Currently, the aversive taste of drugs is tested in late stages of clinical trials. This can result in the need to reformulate, potentially resulting in the use of more animals for additional toxicity trials, increased financial costs and a delay in release to the market. Here we present BitterIntense, a machine learning tool that classifies molecules into “very bitter” or “not very bitter”, based on their chemical structure. The model, trained on chemically diverse compounds, has above 80% accuracy on several test sets. BitterIntense suggests that intense bitterness does not correlate with toxicity and hepatotoxicity of drugs and that the prevalence of very bitter compounds among drugs is lower than among microbial compounds. BitterIntense allows quick and easy prediction of strong bitterness of compounds of interest for food and pharma industries. We estimate that implementation of BitterIntense or similar tools early in drug discovery and development process may lead to reduction in delays, in animal use and in overall financial burden.Significance StatementDrug development integrates increasingly sophisticated technologies, but extreme bitterness of drugs remains a poorly addressed cause of medicine regimen incompletion. Reformulating the drug can result in delays in the development of a potential medicine, increasing the lead time to the patients. It might also require the use of extra animals in toxicity trials and lead to increased costs for pharma companies. We have developed a computational predictor for intense bitterness, that has above 80% accuracy. Applying the classifier to annotated datasets suggests that intense bitterness does not correlate with toxicity and hepatotoxicity of drugs. BitterIntense can be used in the early stages of drug development to identify drug candidates that require bitterness masking, and thus reduce animal use, time and monetary loss.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
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.

Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

2021 ◽  
Vol 21 ◽  
Author(s):  
Diana M. Herrera-Ibatá
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Molecular Descriptors ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Drug Candidates ◽  
Protein Research ◽  
New Compounds

: Recently different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing great amounts of chemical data obtaining outcomes to find potential drug candidates.

scholarly journals Cryptosporidiosis: A Potential Anti-diarrheal Natural Product Drug Discovery Journey in Ghana, West Africa

2020 ◽  
pp. 85-93
Author(s):  
Senyo K. Botchie ◽  
Andrew G. Mtewa ◽  
Irene Ayi
Keyword(s):  
Developing Countries ◽  
Natural Products ◽  
Drug Discovery ◽  
Drug Development ◽  
Natural Product ◽  
Causative Organism ◽  
Drug Candidates ◽  
Cause Of Deaths

The overwhelming resistance to current drugs and the exhaustion of drug development interventions, as well as synthetic libraries, have compelled researchers to resort to the use of novel drug candidates derived from natural products. Cryptosporidium, the causative organism of Cryptosporidiosis, is no exception. The diarrhea-causing parasite is known to be the leading cause of deaths in children below age 5 in developing countries like Ghana and second to rotavirus as the causative agent for diarrhea in newborn calves and infants. Currently, the only FDA approved drug for the treatment of Cryptosporidiosis is Nitazoxanide. It is, therefore, needful to develop novel alternative candidates as it could aid in the decrease in child mortality and malnutrition in developing countries. Even though there have been significant limitations into anti-cryptosporidial drug development in vitro and in vivo, essential advancements are being made of which this article addresses the need for research into natural products. Some studies outlined in this paper has stated potential plant extracts showing anti-cryptosporidiosis efficacy. With the wealth of medicinal plant products and Cryptosporidium in vitro culture expertise available in our labs at Noguchi Memorial Institute for Medical research we are certain of making potential significant strides in the world of natural product Cryptosporidium drug discovery in Africa.

scholarly journals Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection

2021 ◽  
Vol 8 ◽  
Author(s):  
Biswa Mohan Sahoo ◽  
B. V. V. Ravi Kumar ◽  
J. Sruti ◽  
Manoj Kumar Mahapatra ◽  
Bimal K. Banik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
Free Energy Calculations ◽  
Pharmacokinetic Property ◽  
The Novel ◽  
Drug Candidates ◽  
Drug Molecules

Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.

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>

scholarly journals Nanotechnology in Preclinical and Clinical Drug Development

2018 ◽  
Vol 1 (1) ◽  
pp. 73-93 ◽  
Author(s):  
Cristian Vilos
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Targeted Delivery ◽  
Medical Diagnostics ◽  
Strong Impact ◽  
Drug Candidates ◽  
Clinical Drug Development ◽  
Novel Approach ◽  
Organ Specific ◽  
Clinical Drug

Nanotechnology is generating a strong impact in preclinical and clinical drug development. The diversity of current nanotechnologies offers a broad platform used to enhance the performance of drug discovery screening, to develop sensitive and specific methods used to unveil the mechanisms behind the actions of drugs, to determine the function and interaction between molecules, and to study the physiological and pathological changes of cellular components. In addition, advancements in nanobiotechnology have led to the design of new nanomaterial-based drug candidates that present a novel approach to medical diagnostics and therapeutics. The biocompatible nanoarchitecture of the marketed nanocarriers used for drug delivery has increased the solubility and effectiveness of classical drugs, and has provided the technology required for the targeted delivery of encapsulated tissue-organ specific therapeutics. Because of its effect on drug development, nanotechnology serves as the foundation for many future medical endeavors. This article provides an overview of the basics of nanobiotechnology, and discusses its applications in drug discovery, design, and delivery systems.

New Perspectives of Machine Learning in Drug Discovery

2020 ◽  
Vol 27 ◽  
Author(s):  
Simona Musella ◽  
Giulio Verna ◽  
Alessio Fasano ◽  
Simone Di Micco
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Structural Design ◽  
Data Sets ◽  
Future Perspectives ◽  
Drug Candidates ◽  
Computer Aided ◽  
Information Interpretation ◽  
The Impact ◽  
Complex Manner

: Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to clinical trial. This approach is harnessing the impact of computer-aided drug discovery thanks to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed.

scholarly journals Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review

2018 ◽  
Vol 24 (28) ◽  
pp. 3347-3358 ◽  
Author(s):  
Kristy A. Carpenter ◽  
Xudong Huang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Development ◽  
Current Knowledge ◽  
Binding Activity ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Drug Leads

Background: Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. Objective: The study aims to review ML-based methods used for VS and applications to Alzheimer’s Disease (AD) drug discovery. Methods: To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). Results: All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks – and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Conclusion: Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development.

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