Molecular similarity: Theory, applications, and perspectives.

Area of Science:

• Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Molecular similarity is a fundamental concept in chemistry.
• Similarity measures are crucial for Machine Learning (ML) in data-intensive chemical research.
• Applications span various fields, including drug design and chemical space exploration.

Purpose of the Study:

• To discuss the multifaceted role of molecular similarity in contemporary chemical research.
• To highlight the importance of similarity measures in Machine Learning (ML) algorithms.
• To cover recent advancements in comparing large molecular libraries.

Main Methods:

• Literature review and conceptual discussion.
• Analysis of Machine Learning (ML) applications in chemistry.
• Exploration of similarity metrics for large-scale data.

Main Results:

• Molecular similarity underpins ML-driven supervised and unsupervised learning in chemistry.
• It is essential for drug design, chemical space exploration, and generative chemistry.
• Efficient comparison of large molecular libraries is an active area of development.

Conclusions:

• Molecular similarity remains a cornerstone of chemical understanding and innovation.
• Its role is amplified in the era of big data and Machine Learning (ML).
• Future research will likely focus on scalable and robust similarity assessment methods.