Big Data in Chemistry: Problems and Answers - Neha Sahu; Rizwan Arif (International Journal of Cheminformatics, 2024)
The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, privacy, and reproducibility. Solutions to these challenges lie in the development of robust data architectures, machine learning algorithms, and artificial intelligence (AI) frameworks that can handle complex chemical data. High-performance computing and cloud-based infrastructures enable the large-scale storage and processing of data, while cheminformatics and data mining techniques help extract meaningful patterns. Interdisciplinary collaboration is crucial, bringing together chemistry, data science, and computer science to overcome these hurdles. Finally, the establishment of standardized databases and open-access platforms is vital for promoting data sharing and accelerating scientific discoveries. In conclusion, while big data in chemistry poses significant challenges, innovative technological and collaborative approaches provide promising solutions. Harnessing the full potential of big data can lead to breakthroughs in chemical research, materials discovery, drug design, and environmental solutions.
Keywords: Big data in chemistry; Cheminformatics; Data integration; interdisciplinary collaboration; machine learning in chemistry