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

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 most appropriate position when encountering a new molecule, without spending a lot of time trying different positions and scoring them."

III. The EquBind model has been successfully applied in the industry, and the authors expect more feedback

The model caught the attention of Pat Walters, chief data officer of therapy company Relay. Walters suggested that Hannes Stärk's group use the model for drug development in the treatment of lung cancer, leukemia and gastrointestinal tumors. Typically, protein ligands for drugs in these areas are difficult to dock using most traditional methods, but EquBind was able to get them to dock successfully.

EquBind offers a unique solution to the protein docking problem by solving problems such as structure prediction and binding site identification," said Walters. This approach makes good use of the 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 can not only improve the efficiency and success rate of drug development, but also hopefully 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.