State of the Art and of Outlook of Data Science and Machine Learning in Organic Chemistry

Abstract

The use of data science, artificial intelligence, and big data in the field of chemistry has grown in recent years to speed up the discovery of new materials, drugs, synthetic substances and automate compound identification. Machine learning and data science are commonly used in organic chemistry to predict biological and physical-chemical properties of molecules and are referred to as QSAR (for biological properties) and QSPR (for non-biological properties). In addition, data science and machine learning have advanced the optimization of molecular properties, synthetic pathways, and even design of novel compounds. These models can learn the underlying patterns of molecular structures and generate new compounds with desirable properties. Machine Learning use is increasing in chemistry and the field is rapidly adopting state-of-the-art ML algorithms and tools such as deep learning, tensors and transformers to solve and model chemical problems. The application of data science and machine learning, particularly deep learning, is playing a significant role in advancing research in organic chemistry