Artificial Intelligence (AI) Applications in Chemistry

Ishita Naik; Dishita Naik; Nitin Naik

doi:10.1007/978-3-031-47508-5_42

Artificial Intelligence (AI) Applications in Chemistry

Ishita Naik, Dishita Naik, Nitin Naik

Software Engineering & Cybersecurity

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

The production of chemicals is a complex task due to the highly nonlinear behaviour of chemical processes; therefore, traditional approaches may not be very effective in developing or predicting such processes and their outcomes at optimal level. Consequently, it has always been challenging to find ways to improve efficiency and productivity while reducing the time and cost. Artificial Intelligence (AI) techniques are becoming valuable in chemistry due to several reasons such as easy to learn and use, simple implementation, easy designing, effectiveness, generality, robustness, and flexibility. AI is comprised of several techniques within it, such as artificial neural networks, evolutionary algorithms and fuzzy logic. AI techniques have been widely used in various areas of chemistry including molecule design, molecular property prediction, retrosynthesis, reaction outcome prediction and reaction conditions prediction. Therefore, this paper investigates AI applications in the aforementioned areas, wherein it explains about each aforementioned area with a suitable example, limitations of traditional techniques, and types of AI techniques which are utilised within those areas.

Original language	English
Title of host publication	Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK
Editors	Nitin Naik, Paul Jenkins, Paul Grace, Longzhi Yang, Shaligram Prajapat
Publisher	Springer
Pages	545-557
Number of pages	13
ISBN (Electronic)	9783031475085
ISBN (Print)	9783031475078
DOIs	https://doi.org/10.1007/978-3-031-47508-5_42
Publication status	Published - 1 Feb 2024

Publication series

Name	Advances in Computational Intelligence Systems
Publisher	Springer
Volume	1453
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Keywords

Artificial Intelligence
AI
De Novo Design
Molecule Design
Molecular Property Prediction
Retrosynthesis
Reaction Outcome Prediction
Reaction Conditions Prediction
QSAR
QSPR

Access to Document

10.1007/978-3-031-47508-5_42

Cite this

Naik, I., Naik, D., & Naik, N. (2024). Artificial Intelligence (AI) Applications in Chemistry. In N. Naik, P. Jenkins, P. Grace, L. Yang, & S. Prajapat (Eds.), Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK (pp. 545-557). (Advances in Computational Intelligence Systems; Vol. 1453). Springer. https://doi.org/10.1007/978-3-031-47508-5_42

Naik, Ishita ; Naik, Dishita ; Naik, Nitin. / Artificial Intelligence (AI) Applications in Chemistry. Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK. editor / Nitin Naik ; Paul Jenkins ; Paul Grace ; Longzhi Yang ; Shaligram Prajapat. Springer, 2024. pp. 545-557 (Advances in Computational Intelligence Systems).

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title = "Artificial Intelligence (AI) Applications in Chemistry",

abstract = "The production of chemicals is a complex task due to the highly nonlinear behaviour of chemical processes; therefore, traditional approaches may not be very effective in developing or predicting such processes and their outcomes at optimal level. Consequently, it has always been challenging to find ways to improve efficiency and productivity while reducing the time and cost. Artificial Intelligence (AI) techniques are becoming valuable in chemistry due to several reasons such as easy to learn and use, simple implementation, easy designing, effectiveness, generality, robustness, and flexibility. AI is comprised of several techniques within it, such as artificial neural networks, evolutionary algorithms and fuzzy logic. AI techniques have been widely used in various areas of chemistry including molecule design, molecular property prediction, retrosynthesis, reaction outcome prediction and reaction conditions prediction. Therefore, this paper investigates AI applications in the aforementioned areas, wherein it explains about each aforementioned area with a suitable example, limitations of traditional techniques, and types of AI techniques which are utilised within those areas.",

keywords = "Artificial Intelligence, AI, De Novo Design, Molecule Design, Molecular Property Prediction, Retrosynthesis, Reaction Outcome Prediction, Reaction Conditions Prediction, QSAR, QSPR",

author = "Ishita Naik and Dishita Naik and Nitin Naik",

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Naik, I, Naik, D & Naik, N 2024, Artificial Intelligence (AI) Applications in Chemistry. in N Naik, P Jenkins, P Grace, L Yang & S Prajapat (eds), Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK. Advances in Computational Intelligence Systems, vol. 1453, Springer, pp. 545-557. https://doi.org/10.1007/978-3-031-47508-5_42

Artificial Intelligence (AI) Applications in Chemistry. / Naik, Ishita; Naik, Dishita; Naik, Nitin.
Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK. ed. / Nitin Naik; Paul Jenkins; Paul Grace; Longzhi Yang; Shaligram Prajapat. Springer, 2024. p. 545-557 (Advances in Computational Intelligence Systems; Vol. 1453).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Naik, Ishita

AU - Naik, Dishita

AU - Naik, Nitin

PY - 2024/2/1

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N2 - The production of chemicals is a complex task due to the highly nonlinear behaviour of chemical processes; therefore, traditional approaches may not be very effective in developing or predicting such processes and their outcomes at optimal level. Consequently, it has always been challenging to find ways to improve efficiency and productivity while reducing the time and cost. Artificial Intelligence (AI) techniques are becoming valuable in chemistry due to several reasons such as easy to learn and use, simple implementation, easy designing, effectiveness, generality, robustness, and flexibility. AI is comprised of several techniques within it, such as artificial neural networks, evolutionary algorithms and fuzzy logic. AI techniques have been widely used in various areas of chemistry including molecule design, molecular property prediction, retrosynthesis, reaction outcome prediction and reaction conditions prediction. Therefore, this paper investigates AI applications in the aforementioned areas, wherein it explains about each aforementioned area with a suitable example, limitations of traditional techniques, and types of AI techniques which are utilised within those areas.

AB - The production of chemicals is a complex task due to the highly nonlinear behaviour of chemical processes; therefore, traditional approaches may not be very effective in developing or predicting such processes and their outcomes at optimal level. Consequently, it has always been challenging to find ways to improve efficiency and productivity while reducing the time and cost. Artificial Intelligence (AI) techniques are becoming valuable in chemistry due to several reasons such as easy to learn and use, simple implementation, easy designing, effectiveness, generality, robustness, and flexibility. AI is comprised of several techniques within it, such as artificial neural networks, evolutionary algorithms and fuzzy logic. AI techniques have been widely used in various areas of chemistry including molecule design, molecular property prediction, retrosynthesis, reaction outcome prediction and reaction conditions prediction. Therefore, this paper investigates AI applications in the aforementioned areas, wherein it explains about each aforementioned area with a suitable example, limitations of traditional techniques, and types of AI techniques which are utilised within those areas.

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KW - Retrosynthesis

KW - Reaction Outcome Prediction

KW - Reaction Conditions Prediction

KW - QSAR

KW - QSPR

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A2 - Grace, Paul

A2 - Yang, Longzhi

A2 - Prajapat, Shaligram

PB - Springer

ER -

Naik I, Naik D, Naik N. Artificial Intelligence (AI) Applications in Chemistry. In Naik N, Jenkins P, Grace P, Yang L, Prajapat S, editors, Contributions Presented at the 22nd UK Workshop on Computational Intelligence (UKCI 2023), September 6–8, 2023, Birmingham, UK. Springer. 2024. p. 545-557. (Advances in Computational Intelligence Systems). Epub 2024 Jan 31. doi: 10.1007/978-3-031-47508-5_42

Artificial Intelligence (AI) Applications in Chemistry

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