scholarly journals Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
José T. Moreira-Filho ◽  
Arthur C. Silva ◽  
Rafael F. Dantas ◽  
Barbara F. Gomes ◽  
Lauro R. Souza Neto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Cost Effective ◽  
Neglected Tropical Disease ◽  
Parasitic Disease ◽  
Drug Candidates ◽  
Single Drug ◽  
Computer Aided ◽  
Prevalent Disease ◽  
Effective Drugs

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Kernel Methods in Chemoinformatics

2011 ◽  
pp. 664-668
Author(s):  
Huma Lodhi
Keyword(s):  
Developing Countries ◽  
Drug Discovery ◽  
Drug Design ◽  
Kernel Methods ◽  
Cost Effective ◽  
Activity Relationship ◽  
World Population ◽  
Structure Activity ◽  
The World ◽  
Effective Drugs

Millions of people are suffering from fatal diseases such as cancer, AIDS, and many other bacterial and viral illnesses. The key issue is now how to design lifesaving and cost-effective drugs so that the diseases can be cured and prevented. It would also enable the provision of medicines in developing countries, where approximately 80% of the world population lives. Drug design is a discipline of extreme importance in chemoinformatics. Structure-activity relationship (SAR) and quantitative SAR (QSAR) are key drug discovery tasks.

scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

scholarly journals Computer-aided drug discovery

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 630 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Drug Discovery ◽  
Computational Tools ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Discovery Research ◽  
Current Trends ◽  
Drug Discovery Research ◽  
Computer Aided ◽  
Computational Drug Discovery ◽  
Different Levels

Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

COMPUTER-AIDED DRUG DISCOVERY: FROM TARGET PROTEINS TO DRUG CANDIDATES

1998 ◽  
Author(s):  
J. Bajorath ◽  
T. E. Klein ◽  
T. P. Lybrand ◽  
J. Novotny
Keyword(s):  
Drug Discovery ◽  
Target Proteins ◽  
Drug Candidates ◽  
Computer Aided

scholarly journals Yeast as a Potential Vehicle for Neglected Tropical Disease Drug Discovery

2014 ◽  
Vol 20 (1) ◽  
pp. 56-63 ◽  
Author(s):  
P.W. Denny ◽  
P.G. Steel
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cost Effective ◽  
Eukaryotic Cell ◽  
Tropical Disease ◽  
Neglected Tropical Disease ◽  
Cellular Context ◽  
Basic Approaches

High-throughput screening (HTS) efforts for neglected tropical disease (NTD) drug discovery have recently received increased attention because several initiatives have begun to attempt to reduce the deficit in new and clinically acceptable therapies for this spectrum of infectious diseases. HTS primarily uses two basic approaches, cell-based and in vitro target-directed screening. Both of these approaches have problems; for example, cell-based screening does not reveal the target or targets that are hit, whereas in vitro methodologies lack a cellular context. Furthermore, both can be technically challenging, expensive, and difficult to miniaturize for ultra-HTS [(u)HTS]. The application of yeast-based systems may overcome some of these problems and offer a cost-effective platform for target-directed screening within a eukaryotic cell context. Here, we review the advantages and limitations of the technologies that may be used in yeast cell–based, target-directed screening protocols, and we discuss how these are beginning to be used in NTD drug discovery.

scholarly journals Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

2021 ◽  
Vol 9 ◽  
Author(s):  
David A. Winkler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Neglected Tropical Diseases ◽  
Tropical Disease ◽  
Neglected Tropical Disease ◽  
Tropical Diseases ◽  
Entry Barrier ◽  
Ongoing Research ◽  
Technological Developments

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

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 Big Data and Artificial Intelligence Modeling for Drug Discovery

2020 ◽  
Vol 60 (1) ◽  
pp. 573-589 ◽  
Author(s):  
Hao Zhu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Data Sets ◽  
Modeling And Analysis ◽  
The Road ◽  
Drug Candidates ◽  
Modeling Studies

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

scholarly journals Article Commentary: Drug Discovery Benefits from Venomous Clues

2010 ◽  
Vol 3 ◽  
pp. PRI.S6097
Author(s):  
Marc A. Williams
Keyword(s):  
Drug Discovery ◽  
Natural Resources ◽  
Cost Effective ◽  
Biologically Active ◽  
Discovery Research ◽  
Current Perspective ◽  
Drug Discovery Research ◽  
Drug Pipeline ◽  
Effective Drugs ◽  
Novel Drug

Pharmaceutical drug discovery is reliant on innovative research and development approaches that uncover novel agents that could service a drug pipeline. Development of novel drugs to combat human disease is largely dependent on the availability of safe and effective drugs, many of which are under development and evaluation–-the so-called “drug pipeline”. While the costs involved in novel drug discovery, research, development and clinical evaluation are quite significant, one approach that can prove both cost-effective and a viable source of bio-therapeutic agents involves bio-prospecting for therapeutically active compounds commonly found in natural resources including microorganisms, plants, invertebrates or reptiles. In a recent issue of Proteomic Insights, Hang Fai Kwok et al provide a historical and current perspective of proteomic and genomic approaches on lizard venoms that have allowed us to understand and appreciate not only this valuable resource of discovering biologically active molecules, but also to alert us to the need of conservation of such invaluable and beneficial natural resources.

scholarly journals The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Francesco De Chiara ◽  
Ainhoa Ferret-Miñana ◽  
Javier Ramón-Azcón
Keyword(s):  
Artificial Intelligence ◽  
Multiorgan Failure ◽  
Adult Population ◽  
Scoring Systems ◽  
Cost Effective ◽  
Legal Regulation ◽  
Full Potential ◽  
Alcoholic Fatty Liver ◽  
Drug Candidates

Non-alcoholic fatty liver affects about 25% of global adult population. On the long-term, it is associated with extra-hepatic compliances, multiorgan failure, and death. Various invasive and non-invasive methods are employed for its diagnosis such as liver biopsies, CT scan, MRI, and numerous scoring systems. However, the lack of accuracy and reproducibility represents one of the biggest limitations of evaluating the effectiveness of drug candidates in clinical trials. Organ-on-chips (OOC) are emerging as a cost-effective tool to reproduce in vitro the main NAFLD’s pathogenic features for drug screening purposes. Those platforms have reached a high degree of complexity that generate an unprecedented amount of both structured and unstructured data that outpaced our capacity to analyze the results. The addition of artificial intelligence (AI) layer for data analysis and interpretation enables those platforms to reach their full potential. Furthermore, the use of them do not require any ethic and legal regulation. In this review, we discuss the synergy between OOC and AI as one of the most promising ways to unveil potential therapeutic targets as well as the complex mechanism(s) underlying NAFLD.

Sign in / Sign up

Export Citation Format

Share Document