Artificial Intelligent (AI) and Machine Learning (ML) are developing significant advances and gaining importance information processing in a huge number of fields such as chemical, pharmaceutical, biological etc. The Perturbation Theory (PT) is commonly combined with ML in order to generate PTML models and has been used in various disciplines to predict the biological activity of drugs and nanoparticles. In addition, this powerful tool has showed promising results in the field of nanoinformatics. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. Therefore, In this review different type of application of machine learning in nanoparticles involve in neurological diseases will be discussed.
In addition, different startup, spin-off, Small and Medium Enterprises (SMEs), and also some BigPharman, Tech companies, Nanoform-Drug Particle Engineering, Precision Medicine, NanosticsNanotechnology-Baed Diagnostic etc. are developing AI-based nanomedicine, nanotechnology, brain diseases so on. This communication also lists some of these startup companies.
Now we closed the publication phase and launched the post-publication phase of the conference.
REVIEWWWERS'08 Brainstorming Workshop is Now Open from 2023-Jan-01 to 2023-Jan-31.
MOL2NET Committee, Authors, and Validated Social Media Followers Worldwide
Invited to Post Moderated Questions/Answers, Comments, about papers.
These are my Questions (Q) to you, please kindly post your public Answers (A) below
to promote scientific discussion and training of conference readers :
Q1. Taking into account that artificial intelligence is a new tool. What strategies do you think are necessary for more laboratories and companies to start using it?
Q2. In the future, do you think that artificial intelligence could replace the experimental part, totally or partially? Do you think this is an advantage or disadvantage?
Thank you for your kind support. Please make questions to other papers in different Mol2Net congresses
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Papers list: https://mol2net-08.sciforum.net/presentations/view
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Thanks for your comments.
Regarding to the first question, I think the most remarkable aspect of AI/ML is that, it can accelerate the process of decision-making and reduce significantly the experimental resources and less time-consuming.
Regarding the second question. I would say that AI/ML is a useful tool to speed up experimental work and can provide the most likely combination that would be successful based on comparison with the most similar experimental results. However, the model obtained through AI/ML is a prediction that may or may not be correct. Therefore, we need the experimental task to contrast or confirm this prediction. As a consequence, we cannot replace the experimental part with the AI/ML technique.
The advantages of AI/ML are fast decision-making and performing many repetitive works, stressful and complex work completed easily, success ratio is high, more efficiency in short time among others.
The disadvantage is that AI can make humans lazy with its applications automating the majority of the work. Humans tend to get addicted to these inventions which can cause a problem to future generations. In addition, as AI is replacing the majority of the repetitive tasks and other works with robots, human interference is becoming less which will cause a major problem in the employment standards.
