scholarly journals Application of Artificial Intelligence in Chemistry

2021 ◽  
Vol 7 (2) ◽  
pp. 18-19
Author(s):  
Preeti Rai ◽  
Harsha Chatrath
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Advanced Technology ◽  
Molecular Property ◽  
Output Data ◽  
Manual Work ◽  
Strong Bond ◽  
Analysis Prediction

All the problems can be solved with the help of machines mainly computers using algorithm and by interpreting their output data is considered as artificial intelligence (AI). Artificial intelligence is faster than manual work, reduces manpower, more efficient and accurate and used in various field these days and coming up with more advanced technology. With the help of artificial intelligence, drugs can be formulated and produced in an advanced way. New machineries’ used in chemical or pharmaceutical labs are much advanced these days, that reduces the time of the analysis. There is a strong bond between artificial intelligence and chemistry. In the field of chemistry designing new molecules, molecular property detection of molecules and compounds, drug discovery, synthesis and retrosynthesis of molecules, analysis prediction for better and accurate results, all these can be done with the help of artificial intelligence.

scholarly journals Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

2021 ◽  
Author(s):  
Manish Kumar Tripathi ◽  
Abhigyan Nath ◽  
Tej P. Singh ◽  
A. S. Ethayathulla ◽  
Punit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

The Effects of Industry 4.0 on Labor Force Attributes and New Challenges

2020 ◽  
pp. 431-454
Author(s):  
Mehmet Saim Aşçı
Keyword(s):  
Artificial Intelligence ◽  
Labor Force ◽  
Industry 4.0 ◽  
The Other ◽  
Advanced Technology ◽  
Significant Problem ◽  
Production Processes ◽  
Other Hand ◽  
New Challenges

Unmanned factories became a topic of discussion after the concept of Industry 4.0 was first introduced in the Hannover Fair in 2001, and increasing the computerization level in business life and supporting the production processes with advanced technology were determined as targets. In this regard, artificial intelligence and increased automation are expected to create new kinds of jobs in the coming years; however, a significant problem is predicted considering that these changes will invalidate a high number of job types exist today. Thus, the workforce will face a severe unemployment threat. As a result of all of this, radical changes in the work methods, along with means of seeking employment, are now considered. The qualities of the work and the workforce are being transformed along with the organization methods of the production. While on the other hand, it becomes evident that education also has to adapt to this transformation. In this study, the issues the labor might have to face during this period will be discussed, along with what could be done to solve these problems.

scholarly journals Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Nagasundaram Nagarajan ◽  
Edward K. Y. Yapp ◽  
Nguyen Quoc Khanh Le ◽  
Balu Kamaraj ◽  
Abeer Mohammed Al-Subaie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Computational Biology ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Data Sets ◽  
Aggregated Data ◽  
Usable Knowledge ◽  
Knowledge Of Cancer ◽  
Generation Sequencing

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Being well aware of this, the world’s leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

Artificial intelligence in the early stages of drug discovery

2021 ◽  
Vol 698 ◽  
pp. 108730
Author(s):  
Claudio N. Cavasotto ◽  
Juan I. Di Filippo
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Early Stages

scholarly journals Artificial intelligence and big data facilitated targeted drug discovery

2019 ◽  
Vol 4 (4) ◽  
pp. 206-213 ◽  
Author(s):  
Benquan Liu ◽  
Huiqin He ◽  
Hongyi Luo ◽  
Tingting Zhang ◽  
Jingwei Jiang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Three Dimensional ◽  
Data Bank ◽  
The Cancer Genome Atlas ◽  
Biological Databases ◽  
Cancer Genome Atlas ◽  
Targeted Drug ◽  
Targeted Drug Discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

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