MIT Researchers Use AI to Design Next-Generation Therapeutic Drugs
MIT scientists, led by James J. Collins, are leveraging artificial intelligence to accelerate the discovery and design of new therapeutic drugs, particularly antibiotics for multidrug-resistant pathogens. Collaborative efforts at MIT and partner institutions have yielded promising new candidates and established pipelines for AI-driven drug development.
Researchers at the Massachusetts Institute of Technology (MIT) are at the forefront of leveraging artificial intelligence (AI) to accelerate the discovery and development of therapeutic drugs, particularly antibiotics aimed at combating multidrug-resistant pathogens.
James J. Collins, a pioneer in synthetic and systems biology, leads several collaborative initiatives at MIT and affiliated institutions. As the director of the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, Collins has worked with experts including Regina Barzilay and Tommi Jaakkola to combine deep learning approaches with systems microbiology. One of their key achievements has been the AI-driven discovery of halicin, a new antibiotic effective against a broad spectrum of resistant bacteria. This work, showcased in the journal Cell in 2020, illustrates the potential for interdisciplinary teams to harness AI in addressing urgent global health challenges.
Another partnership with Donald Ingber at Harvard’s Wyss Institute leverages organs-on-chips technology. This advanced experimental platform mimics human tissue conditions, allowing more accurate assessment of AI-discovered and AI-designed drug candidates.
A recent study by the Collins Lab at MIT demonstrated how generative AI—an approach where algorithms create entirely new data or structures—can be used to design innovative antibiotic candidates from scratch. The researchers deployed genetic algorithms and variational autoencoders, a type of neural network used for unsupervised learning, to generate and filter millions of potential molecules. Of 24 synthesized candidates, seven exhibited selective antibacterial activity. Notably, one compound, NG1, successfully eradicated drug-resistant Neisseria gonorrhoeae, while another, DN1, targeted methicillin-resistant Staphylococcus aureus (MRSA) in animal models. Both candidates demonstrated low toxicity and a reduced risk of resistance development.
Looking forward, the MIT team aims to integrate deep learning further with high-throughput biological testing. This combined approach is expected to streamline the identification of antibiotics that are safe, effective, and display favorable drug-like properties, helping to counteract the rapid emergence of resistance among pathogenic bacteria.
To bridge the gap between laboratory discovery and clinical development, Collins co-founded Phare Bio, a nonprofit entity focused on AI-assisted antibiotic discovery. Through the Antibiotics-AI Project and with the support of a grant from the U.S. Advanced Research Projects Agency for Health (ARPA-H), the group is developing 15 new AI-designed compounds as pre-clinical drug candidates. By linking computational design, experimental validation, and partnerships with industry, nonprofit, and government sectors, this pipeline aims to deliver much-needed therapies more rapidly to address the growing global threat of antibiotic resistance.
For further details, see the original coverage at news.mit.edu.
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