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Toward Artificial Intelligence Era in Drug Discovery and Design
Published:
10 March 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress CHEMBIO.INFO-09: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress München, GR-Chapel Hill, USA, 2023.
https://doi.org/10.3390/mol2net-09-14141
(registering DOI)
Abstract:
In the last decades, we have experienced a revolution in data science in terms of the huge amount of data to be analyzed (era of big data) and the availability of high-performance processors. In drug discovery, this scenario is not different: the large volume of data (chemical, biological, etc.) along with the automation of techniques have generated a fertile ground for the use of artificial (or computational) intelligence/Machine Leaning (AI/ML). This powerful tool helped the researchers to achieve several major theoretical and applied breakthroughs. In this mini-review, recent research work of AI/ML in drug discovery and design will be introduced.
Keywords: Machine Leaning; drug discovery; big data; data science
Comments on this paper
Ajit Singh
27 December 2023
The research paper elegantly navigates the landscape of contemporary data science, emphasizing the transformative impact of the big data era and high-performance processors on drug discovery. The abstract adeptly sets the stage, highlighting the symbiosis of vast data sets and automated techniques, creating an ideal environment for the integration of artificial intelligence and machine learning (AI/ML) in this field.
The paper stands out in its exploration of recent advancements in AI/ML applied to drug discovery and design, effectively showcasing the pivotal role these technologies play in both theoretical frameworks and practical breakthroughs. By seamlessly weaving together the realms of chemistry, biology, and computational intelligence, the researchers have successfully portrayed the synergistic relationship between data-driven methodologies and innovative AI/ML applications.
The concise yet comprehensive nature of this mini-review is commendable, offering readers a valuable overview of the strides made in leveraging AI/ML for drug discovery. The paper's contribution to the understanding of the evolving landscape in pharmaceutical research and development is evident, making it a noteworthy addition to the scholarly discourse in this domain.
The paper stands out in its exploration of recent advancements in AI/ML applied to drug discovery and design, effectively showcasing the pivotal role these technologies play in both theoretical frameworks and practical breakthroughs. By seamlessly weaving together the realms of chemistry, biology, and computational intelligence, the researchers have successfully portrayed the synergistic relationship between data-driven methodologies and innovative AI/ML applications.
The concise yet comprehensive nature of this mini-review is commendable, offering readers a valuable overview of the strides made in leveraging AI/ML for drug discovery. The paper's contribution to the understanding of the evolving landscape in pharmaceutical research and development is evident, making it a noteworthy addition to the scholarly discourse in this domain.
Humbert G. Díaz
1 January 2024
Dear author(s), Happy New Year 24, Thank you for your contribution to our conference!!!
We have a question for you, you can read and answer bellow.
Question for Authors:
According to your experience what are the SWOT on using Phyton machine learning libraries vs. user friendly interfaces for drug discovery?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
We have a question for you, you can read and answer bellow.
Question for Authors:
According to your experience what are the SWOT on using Phyton machine learning libraries vs. user friendly interfaces for drug discovery?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
Shan He
2 January 2024
The utilization of Python machine learning libraries and user-friendly interfaces in drug discovery each present distinct strengths, weaknesses, opportunities, and threats (SWOT):
Python Machine Learning Libraries:
Strengths: They are highly flexible, efficient for handling large datasets, and have extensive community support for customization.
Weaknesses: Requires programming expertise, can be resource-intensive for complex models.
Opportunities: Allows innovation, integration with other tools.
Threats: May have a steep learning curve and require ongoing maintenance.
User-Friendly Interfaces:
Strengths: Easy to use, accessible to non-programmers, enables quick prototyping.
Weaknesses: Limited customization, dependency on vendor updates.
Opportunities: Facilitates collaboration, particularly across disciplines.
Threats: Might limit advanced techniques, dependence on proprietary software.
Python Machine Learning Libraries:
Strengths: They are highly flexible, efficient for handling large datasets, and have extensive community support for customization.
Weaknesses: Requires programming expertise, can be resource-intensive for complex models.
Opportunities: Allows innovation, integration with other tools.
Threats: May have a steep learning curve and require ongoing maintenance.
User-Friendly Interfaces:
Strengths: Easy to use, accessible to non-programmers, enables quick prototyping.
Weaknesses: Limited customization, dependency on vendor updates.
Opportunities: Facilitates collaboration, particularly across disciplines.
Threats: Might limit advanced techniques, dependence on proprietary software.
