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MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses. Originally published by Alex Shipps | MIT CSAIL in MT News, on September 15, 2025 Fetal SMPL was trained on 20,000 MRI volumes to predict the location and size of a fetus and create sculpture-like 3D representations. The approach could enable doctors to precisely measure things like the size of a baby’s head and compare these metrics with healthy fetuses at the same age. Credits: Image: Alex Shipps and Yingcheng Liu/MIT CSAIL    For pregnant women, ultrasounds are an informative (and sometimes necessary) procedure . They typically produce two-dimensional black-and-white scans of fetuses that can reveal key insights , including biological sex, approximate size, and abnormalities like heart issues or cleft lip. If your doctor wants a closer look , they may use magnetic resonance imaging (MRI) , which us...

Originally published by Ian Haydon, University of Washington , February 22,2023   An artist's imaginative conception of the idea of light-emitting enzymes. Credit: Ian Haydon / Institute for Protein Design For the first time, scientists have used machine learning to create brand-new enzymes , which are proteins that accelerate chemical reactions. This is an important step in the field of protein design , as new enzymes could have many uses across medicine and industrial manufacturing . "Living organisms are remarkable chemists. Rather than relying on toxic compounds or extreme heat, they use enzymes to break down or build up whatever they need under gentle conditions. New enzymes could put renewable chemicals and biofuels within reach," said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of the 2021 Breakthrough Prize in Life Sciences. Original article 

Model learns how individual amino acids determine protein function Technique could improve machine-learning tasks in protein design, drug testing, and other applications. Rob Matheson | MIT News Office Publication Date: March 22, 2019   A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research. Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein’s biological function. Knowing a protein’s 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs. Original article