Chemists are increasingly employing artificial intelligence (AI) to accomplish diverse lists of tasks. Initially, the need to enhance the drug development process and lower its massive development costs and time pushed the AI or computer-based research in chemistry. In this article, we have described how computer-based intelligence is utilised in chemistry to enhance knowledge, which further helps to do more creative or innovative research in minimal time.
1. Introduction
Artificial intelligence (AI) is used in various tasks, such as human speech recognition, creating strategic games, autonomously operating cars, chemistry, etc. Artificial intelligence and machine learning have demonstrated their potential role in predicting chemistry and synthetic planning of small molecules; at least a few reports of companies employing in silico synthetic planning in their overall approach to accessing target molecules. In chemistry, data has a significant role; the accumulated data is interpreted, and the knowledge and findings are derived. Researchers and scientists conduct many experiments focused on the chemistry of molecules or atoms and collect data. The collected data is then used to study atoms or molecules’ chemical and physical properties, reactivity in chemical reactions, or biological activity. Also, the collected data were used by analogy to make predictions or derive models for the principles underlying the data. This article attempts to show how chemoinformatics methods have been instrumental in using chemistry-related data to enhance our knowledge and how that knowledge can be used to accelerate chemical innovation. [1]
2. Role of AI in Learning in Chemistry
Due to the experiments in chemistry on a considerable scale, a massive increase in chemical data can be seen. Fortunately, along with this growth, computer-based technology has also advanced and is becoming more powerful day by day. Due to the advancement of computers, it is being used to solve mathematical operations in the equations of quantum mechanics. It gives us more convenience in accurately doing the complex quantum mechanics calculations of chemical and physical data. However, this type of learning is considered deductive learning. In this type of learning, data is produced using predefined information. However, computers are used to perform logical operations, so if we develop software that can convert data into information and use that information to enhance knowledge, then the properties of any molecules can be predicted. This type of learning is known as inductive learning. In this, data obtained is used to give information. Figure 1 represents deductive and inductive learning. [1]
Figure 1: Deductive and Inductive learning.[1]
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