Large language models may speed drug discovery

Computational models have been a major time saver when it comes to predicting which protein molecules could make effective drugs, but many of those methods themselves take a lot of time and computing power. Now researchers at MIT and Tufts have devised an alternative approach based on an algorithm known as a large language model, which can figure out which words (or, in this case, amino acids) are most likely to appear together. The model can match target proteins and potential drug molecules without the computationally intensive step of calculating each protein's 3D structure from its amino acid sequence. The resulting system can screen more than 100 million drug-protein pairs in a single day. The researchers tested their model by screening a library of about 4,700 candidate drug molecules for their ability to bind to a set of 51 enzymes.