scholarly journals MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0124600 ◽  
Author(s):  
Selcuk Korkmaz ◽  
Gokmen Zararsiz ◽  
Dincer Goksuluk
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Early Phase ◽  
Web Tool ◽  
Drug Discovery And Development

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

2018 ◽  
Vol 8 (5) ◽  
pp. 504-509 ◽  
Author(s):  
Surabhi Surabhi ◽  
BK Singh
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Drug Discovery And Development ◽  
Research Areas ◽  
Computer Aided ◽  
Pharmaceutical Industries

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Spread and Dynamics of COVID-19 in the Kingdom of Saudi Arabia and Four Other Countries in the Early Phase: A Study for the Development of the Advanced Health System

2021 ◽  
Author(s):  
Shahanavaj Khan ◽  
Asimul Islam ◽  
Ahmad Firoz ◽  
Anis Ahmad Chaudhary ◽  
Mohammad Amjad Kamal ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Health Policy ◽  
Drug Discovery ◽  
Early Phase ◽  
Kingdom Of Saudi Arabia ◽  
Drug Discovery And Development ◽  
First Case ◽  
Daily Report ◽  
The World

Abstract BackgroundThe epidemic of 2019 novel coronavirus (SARS-CoV-2) is challenge to the world which was at first confirmed in the Wuhan city of China in December 2019. It was declared a pandemic by the World Health Organization (WHO) in March 2020. In the current work we evaluated effect of health policy of Saudi Arabia for the management of COVID-19 pandemic in early phase and compared to other four countries. MethodBased on the Ministry of Health, Kingdom of Saudi Arabia (KSA) data, the summary of daily report of COVID-19 was prepared from 02 March to 30 April 2020. Further, the daily report of enhancement in cases and recovery of the patients was also summarized. Moreover, the incidence, death and recoveries of COVID-19 cases in KSA were compared with major infected country including China, Italy, Spain and United State of America (USA). The important role of artificial intelligence was shown for the development of drug targets against to infectious diseases Results In KSA, the first case of COVID-19 was reported on 02 March 2020. Since then, it has affected 22,753 persons till the end of the April 2020. Also, the results showed that the infection rate of COVID-19 increased continuously during the current period of study in KSA. Nevertheless, the rate of death due to COVID-19 is much less with comparison to China, Italy, Spain, and USA due to good medical facilities along with quick action by the government of KSA after the emergence of first case. There is a dire need to develop new platforms and approaches to combat new and old diseases including COVID-19 at warp speed when compared to traditional approaches. DeepDrug’s approach to drug discovery and development showed brighter future towards the discovery of novel drugs against infectious diseases including COVID-19.ConclusionCurrently, there is higher probability of COVID-19 spread at any place. Therefore good health policy, precautionary measures and medical facility of whole nations should be excellent to combat against the COVID-19 pandemic until the reliable vaccine or antiviral drug developed for the proper treatment of virus. The artificial intelligence (AI) based available process might be very helpful for the drug discovery and development against of old and newly discovered diseases including COVID-19.

scholarly journals Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Nadia Terranova ◽  
Karthik Venkatakrishnan ◽  
Lisa J. Benincosa
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Precision Medicine ◽  
Translational Medicine ◽  
Pharmaceutical Research ◽  
Current Status ◽  
Data Sets ◽  
Patient Centered ◽  
Drug Discovery And Development ◽  
Reverse Translation

AbstractThe exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Insights into Machine Learning-based approaches for Virtual Screening in Drug Discovery: Existing strategies and streamlining through FP-CADD

2020 ◽  
Vol 17 ◽  
Author(s):  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser Daanial Khan
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Swot Analysis ◽  
Peer Review Process ◽  
Mining Machine ◽  
Support Vector ◽  
Learning Approaches ◽  
Computer Aided ◽  
Drug Designing

Background: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS. Objectives: This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article. Conclusions: By this thorough study, we have observed that in LBVS algorithms, Support vector machines (SVM) and Random forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

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