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In recent years, deep learning has become an invaluable resource for chemists, notably in the areas of chemical reaction prediction and synthesis design. This abstract gives a quick summary of how deep learning is used in certain contexts. Predicting the result of chemical reactions is a difficult undertaking that requires much research. Training deep learning models on massive databases of known responses allows them to discover patterns and correlations. Examples of such models are graph neural networks, convolutional neural networks, and recurrent neural networks. Some of the things these models can do is anticipate key products, reaction processes, and yields by using the structural and contextual information of reactants. The goal of synthesis planning is to create a reaction plan that will successfully synthesize a desired molecule. To predict possible reactions for a given target molecule, deep learning algorithms study and learn from large reaction databases. These models think about things like whether or not a reaction can happen, under what circumstances, and with what reagents. Deep learning models may speed up the process of inventing novel chemical synthesis by proposing efficient and practical synthesis pathways using reinforcement learning or optimization approaches. Despite its potential, deep learning faces a number of obstacles and restrictions. The interpretability of model predictions, the ability to generalize to novel reaction types, the integration of safety and feasibility limitations, and the availability of extensive and trustworthy reaction datasets all play a role. Despite these obstacles, deep learning is set to transform chemical reaction prediction and synthesis planning thanks to continuous research and breakthroughs. It is possible for scientists to leverage the power of AI by combining deep learning with curated databases and domain knowledge to speed up the discovery of novel reactions and aid in the creation of more efficient and sustainable synthesis pathways. [ABSTRACT FROM AUTHOR]

The application of Big Data Analytics, sometimes referred to as BDA, is becoming an increasingly crucial part of the field of supply chain management (commonly known as SCM), which is becoming an increasingly important field overall. This is due to the fact that B is flexible enough to be utilized in a diverse range of SCM-related processes, which is the rationale behind the aforementioned remark. This category includes a wide variety of different types of employment, such as researching trends, forecasting demand, and evaluating customer behavior, to name just a few examples. With the help of this literature analysis, our goals are to categorize the predictive Big Data Analytics applications in supply chain demand forecasting, identify the research gaps, and provide some ideas for further investigation. In the tough economic environment of today, firms have used a wide array of targeted marketing tactics in order to maintain or expand their profit margin while simultaneously being one step ahead of their competitors. The usage of forecasting models is a significant component of the practice of precision marketing which aims to better comprehend and satisfy the requirements of the customers it serves. The evaluation of consumption patterns and preferences on the basis of data collected from customers and records of transactions is receiving an increasing amount of focus and attention as a result of this trend. This is done to ensure that product supply chains (SC) are managed in the most effective manner possible. The basic purpose of supply chain management, commonly known as SCM, is to exert command over the flow of products, services, and information as it goes from the locations where it was made all the way to the individuals who will ultimately buy it. The presumption that capacity, demand, and cost are all deterministic is the foundation of the great majority of research on the application of SCM to problems that occur in the real world. In point of fact, the presence of uncertainty is made worse by a variety of issues, some of which include but are not limited to varying customer demand, management of the supply chain, organizational risks, and lead times. To be more specific, demand ambiguity has a significant impact on SC performance and has far-reaching repercussions for the production scheduling, inventory planning, and transportation organization. This is because demand ambiguity makes it difficult to accurately predict future demand. In addition to this, the unpredictability of the market's demand might have a negative impact on the efficiency of SC's operations. As a direct result of this fact, demand forecasting is an essential instrument for the mitigation of chain risks. [ABSTRACT FROM AUTHOR]