Artificial Intelligence (AI) in Chemical Market is Increasing Faster

Ottawa, March 09, 2023 (GLOBE NEWSWIRE) -- Precedence Research, recently announced report on "Artificial Intelligence (AI) in Chemicals Market (By Type: Hardware, Software, Services; By Application: Molecule Design, Retrosynthesis, Reaction Outcome Prediction, Reaction Conditions Prediction, Chemical Reaction Optimization) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032".

Researchers are heavily focusing on artificial intelligence to carry out various tasks. Initially, AI practiced in chemistry research was motivated primarily to accelerate drug discovery while reducing the massive expenses and time to launch new pharmaceuticals. In addition, AI can predict future material costs. It makes the manufacturing process more marketable and significantly reduces the company's losses. In the chemical industry, AI can reduce prediction inaccuracy by approximately 50% when compared with human forecasting. The intelligent industry 4.0 approach has the potential to have a significant influence on this sector, not only due to its ability to maximize profit and productivity but also due to its ability to decrease chemical companies' impact on the environment.

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Regional Landscape

Due to increased understanding of digitization and R&D funding, North America is predicted to hold the largest share of the AI industry. Government agencies contribute to the development of trust in AI-based processes by providing advancement guidance.

During the forecast period, the region of Asia-Pacific is expected to have the leading position. Factors such as improving chemical industries, increased R&D investments in production processes, and rapid adoption of modern techniques by chemical sectors in developing nations such as Japan, Indonesia, India, and China are predicted to propel regional AI in the chemical industry. AI implementation reduces the R&D difference in the drug production process and aids in targeted drug production. As a result, chemical companies are turning to artificial intelligence (AI) to increase their market share. AI for drug development is a technique that employs bots to mimic human intelligence in order to solve complex drug development challenges.

Scope of the Report

Type Landscape

The software sector contributed the most share in 2022 and is expected to continue to dominate throughout the projected period because of the constant software evolution that serves the needs of the chemical sector. The dominance of this segment has resulted from increased sales for software as a result of a growing need for drug research and development.

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Service Landscape

On the other hand, services are expected to grow at the fastest rate during the forecast period. The requirement for greater awareness of AI-based hardware as well as software activity among life science experts is increasing the demand for third-party service operators who deliver technologically trained staff to operate costly AI technologies. During the forecast period, this factor drives the global AI services sector in the chemical market. For instance, ChemAILab offers services for the synthesis of molecular libraries as well as NMR identification.

Application Landscape

Between 2023 and 2032, the molecule design sector is expected to grow at the fastest CAGR. Machine learning has proven to be effective in drug forecasting as well as material discovery. Artificial intelligence also assists in the field of organic photovoltaic (OPV). These machine learning-based techniques aid in the identification of molecules, characteristics, and interactions, as well as the prediction of reaction outcomes.

Reaction Outcome Prediction

After the compound has been produced, we must determine whether it possesses all the qualities of the molecule we aimed to develop. This process is complicated and time-consuming when done manually. However, with the assistance of artificial intelligence, this procedure has become more accessible and less time-consuming. Artificial intelligence-based algorithms were applied to the chemical compound to predict its properties, such as solubility, temperature, melting point, and others.

The BOB is a machine-based model that consists of a large amount of information that is used to predict molecular characteristics such as polarizability, HOMO (highest occupied molecular orbital), and LUMO (lowest unoccupied molecular orbital) of the compound. A machine learning-based program trained to forecast reaction conditions, including any organic reaction. This program predicts the solvent(s), catalyst(s), reagent(s), and temperature.

Market Dynamics

Drivers

Yield Optimization

85% of production problems are caused by minor variations that can be easily controlled and modified utilizing Artificial Intelligence. The chemical sector is heavily reliant on parameters such as temperature and pressure. AI continuously monitors these variables and provides real-time temperature and pressure control guidance to achieve the highest yield. For instance, India Glycols is implementing a variety of Industrial Internet of Things, robotic automation, as well as AI technologies. The objective is to develop leaner, greener production capabilities that improve energy efficiency, manufacturing yield, quality, and throughput.

Enhancing Safety Measures

Chemical manufacturing is among the world's most stringently regulated sectors. It poses a variety of safety risks, and any individual's safety is assured by gathering real-time data as well as incorporating it with advanced analytics solutions. Sensors are utilized to collect data, and that data is then applied to ensure the protection of all individuals in the manufacturing unit. Sensors can detect if or not a person is wearing PPE. For instance, Multibillion-dollar companies such as Dow are using AI tracking to identify and eliminate safety risks.

Restraint

The "Black Box" of AI

AI is well-known for its ability to extract information from massive amounts of data, uncover underlying patterns, as well as make a data-driven judgment. However, while the system consistently produces precise results, there is a significant drawback. The AI system is unable to convey or describe how it came to this conclusion. As a result, the question arises: how can we trust the system in highly sensitive areas such as governance, national security, or high-stakes business ventures?

Recent Developments:

1. Haber plans to launch an AI-powered green chemistry lab in Pune in 2023.
2. Chemical.AI raised approximately $14 million in Series B funding in 2022 to transform R&D in chemistry.
3. XuanZhu BioPharm as well as Chemical.AI Unveil Drug Discovery Collaboration in 2022.
4. In 2022, an AI-powered robot discovered reaction conditions for Suzuki-Miyaura coupling.

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