Scientists have used AI to design proteins with two different states, essentially creating biological transistors that can change their shape depending on inputs, opening up new possibilities for biotechnology and medical solutions.
AI-assisted drug discovery has led to the discovery of a new antibiotic called halicin, which has the potential to kill antibiotic-resistant bacteria, marking a significant breakthrough in addressing the public health issue of superbugs; the use of AI has expedited the drug discovery process by analyzing vast amounts of medical data and predicting the properties of molecules.
Artificial intelligence (AI) techniques, particularly machine learning, are increasingly being used in drug research and development (R&D), with applications expanding beyond small molecules to include large-molecule modalities such as antibodies, gene therapies, and RNA-based therapies. These therapies, which make up a significant portion of the biopharma industry's current and future commercial potential, are expected to represent approximately 50% of the oncology market by revenue in 2030, with the majority coming from antibodies.
An artificial intelligence system has been developed that can describe the smell of compounds based on their molecular structures, with descriptions often similar to those of human experts, providing insights into how the brain interprets smell and aiding in the design of new scents.
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.
Artificial intelligence has the potential to revolutionize the medical industry by quickly discovering new drug candidates and extending human lifespans through therapies that repair damage to cells and tissues, leading to a projected $50 billion AI drug discovery revolution and the possibility of living to 150 years old.
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.
AI is revolutionizing scientific research by accelerating drug discovery, predicting protein structures, improving weather forecasting, controlling nuclear fusion, automating laboratory work, and enhancing data analysis, allowing scientists to explore new frontiers and increase research productivity.
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.
