1200x more efficient, MIT develops new model for AI drug making

MIT researchers have recently developed a new model called EquBind, which can predict the structure of new protein molecules in advance and improve the efficiency of drug development, according to Tech Xplore.

The technology has already been recognized within the industry, and a paper describing it will be accepted at the International Conference on Machine Learning (ICML) in July.

EquBind model can rapidly screen drug-like molecules with 1200 times speedup

Currently, drug development is a long and expensive affair. One of the main reasons is that the cost of developing drugs is very expensive. This cost includes not only billions of dollars of capital investment, but also decades of research time.

And in the process of research and development, 90% of the drugs will be ineffective or too many side effects and development failure, only 10% of the drugs can successfully pass the Food and Drug Administration inspection, was approved for marketing.

As a result, pharmaceutical companies raise the price of successful drugs to compensate for the loss of failed drugs, so the price of some drugs remains high.

If researchers want to develop drugs, they must first find drug-like molecules that have potential for development. Another important reason for the slow progress of drug development is the large number of existing drug-like molecules. Data show that there are 1016 drug-like molecules in existence, a number that far exceeds the computational limits of existing molecular computational models.

To handle such a large number of molecules and speed up the drug development process, Hannes Stärk, a first-year student in MIT's Department of Electrical Engineering and Computer Science, has developed a geometric deep learning model called "EquBind. EquBind runs 1200 times faster than the fastest existing molecular computational docking models and is able to find drug-like molecules much faster.