Machine learning for chemistry: Basics and applications

A review published in Engineering explores the potential of machine learning (ML) in revolutionizing chemical research, providing insights into popular ML algorithms and their applications in chemistry.

phys.org

Relevant topic timeline:

8/23/2023

A map of every possible molecule could be possible with AI

Chemists are developing a chemical map of all possible molecules to accelerate the discovery process for drugs and materials, with the help of artificial intelligence to determine the properties and viability of these molecules.

9/2/2023

AI 'nose' predicts smells from molecular structures

Scientists have developed a machine learning model that can predict the odor profile of a molecule based on its structure, potentially revolutionizing the food, fragrance, and synthetic chemistry industries.

9/4/2023

Machine learning tool simplifies one of the most widely used reactions in the pharmaceutical industry

Illinois researchers and chemists at Hoffman La-Roche have developed a machine learning tool that predicts the best conditions for a high-yielding carbon-nitrogen bond forming reaction, accelerating the process and reducing the need for time-consuming experimentation in the pharmaceutical industry.

9/7/2023

Machine Learning Model Offers More Sustainable Approach to Discover New Magnet Materials

Scientists at Ames National Laboratory have developed a machine learning model to predict new magnet materials without using scarce elements, offering a more sustainable pathway for future technological applications.

9/11/2023

AI and Machine Learning Set to Accelerate Natural Product Drug Discovery

Summary: This article discusses various computational approaches and tools used in the field of natural product discovery, including artificial intelligence, machine learning, and data mining, to analyze and predict the structures, activities, and biosynthetic pathways of natural products. The article also emphasizes the importance of data quality, curation, and community collaboration in advancing natural product research.

9/12/2023

Machine Learning Algorithms Optimize Antibody Drugs by Predicting Modifications

Machine-learning algorithms developed at the University of Michigan can identify problem areas in antibodies that cause them to bind with non-target molecules, enabling researchers to modify the antibodies and optimize their effectiveness in fighting diseases like Parkinson's, Alzheimer's, and colorectal cancer.

9/15/2023

MIT Lab Uses Inspiration from Nature and Automation to Revolutionize Therapeutics Discovery

Professor Brad Pentelute and the Pentelute Lab at MIT are using a combination of chemistry, biology, and engineering to develop new techniques and platforms that have the potential to revolutionize therapeutics, including using nature-inspired research to solve protein delivery problems and building an automated protein printing machine. They are also using machine learning and automation to discover new peptides and proteins that can be used in cancer treatment and other applications, with the goal of rapidly designing molecules with new functions and advancing the field of AI-driven molecule design.

9/19/2023

Pharma Collaboration Could Accelerate AI-Driven Protein Drug Development

The use of generative AI, combined with federated and active learning, can accelerate the development of protein drugs by improving predictions of drug properties and enabling collaboration among biopharmaceutical companies while protecting their competitive interests.