July 14 – 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 is currently high.
In order to process molecules with such large data 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.
EquBind models can accurately predict protein structures and improve drug discovery efficiency
Most of the traditional molecular docking models find drug-like molecules by a method called “ligand-to-protein binding”. Specifically, the model takes in a large number of sample molecules and then allows the ligand to bind to various molecules, which are then scored by the model and ranked to find the most suitable molecule. However, this is a cumbersome process and the model is less efficient in finding drug-like molecules.
Hannes Stärk gives an analogy to this process, saying, “The typical ‘ligand-protein’ approach used to be like trying to get a model to insert a key into a lock with many keyholes, and the model spends a lot of time scoring the suitability of the key and each of the holes and then selecting the most suitable one. and then picking the one that fits best.”
He goes on to explain, “EquBind skips the most time-consuming step and can predict the most suitable ‘keyhole’ in advance when a new molecule is encountered, which is called ‘blind docking. EquBind has a built-in geometric inference algorithm that helps the model learn the basic structure of the molecule. This algorithm allows EquBind to directly predict the best fit when encountering a new molecule, without spending a lot of time trying different positions and scoring them.”
The model caught the attention of Pat Walters, chief data officer of the therapeutic company Relay. Walters suggested that Hannes Stärk’s research group use the model for drug development used to treat lung cancer, leukemia and gastrointestinal tumors. Typically, protein ligands for drugs in these areas are difficult to dock with most traditional methods, but EquBind allows them to dock successfully.
EquBind offers a unique solution to the protein docking problem by addressing issues such as structure prediction and binding site identification,” said Walters. This approach makes good use of thousands of publicly available crystal structure information, and EquBind could impact the field in new ways.”
Hannes Stärk, author of the paper publishing the technique, which will be accepted at the International Conference on Machine Learning (ICML) in July, said, “I’m looking forward to receiving some ideas for improvements to the EquBind model at this conference.”
The pharmaceutical field is a natural AI scenario. The long cycle, high cost, and low success rate of new drug development leave a huge place for AI: machines can learn data, mine data, summarize and summarize the laws of drug development outside of expert experience, and then optimize all aspects of the drug development process, which not only can improve the efficiency and success rate of drug development, but also is expected to reduce R&D costs and trial and error costs.
Because of such characteristics and development potential, AI drug making is currently gaining momentum. However, there are some industry insiders who say that AI is only playing a supporting role in the pharmaceutical process, and cannot bypass the inherent processes and mechanisms of the industry, so it is impossible to do what we have been doing for 10 years in two or three years.
But overall, there are still new technological breakthroughs in the field of AI pharmaceuticals, and development is booming.
