Machine Learning in Molecular Sciences: A Renaissance
The Rise and Fall of AI in Molecular Sciences
Research in molecular sciences has witnessed a cycle of rise and fall in the application of Artificial Intelligence (AI) and Machine Learning (ML) methods, particularly artificial neural networks, over the past few decades.
Modern ML Methods and Their Success in Chemistry
In recent years, modern ML methods have experienced a resurgence in popularity and have shown remarkable success in chemistry. The past decade has witnessed a surge in ML applications in scientific research, particularly in the fields of predictive chemistry and synthetic planning of small molecules.
Basic Constituents of ML
This review provides a concise overview of the basic constituents of ML, including supervised learning, unsupervised learning, and reinforcement learning. It explores how these methods can be applied to tasks such as molecular property prediction, reaction prediction, and materials discovery.
Conclusion
The renaissance of ML in molecular sciences is a testament to the transformative power of AI and its potential to revolutionize scientific research. As ML methods continue to advance, we can expect even more groundbreaking discoveries and applications in the years to come.
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