Artificial intelligence model finds potential drug molecules a thousand times faster

Good news! And this is only the beginning! This is possibly a breakthrough! This seems to be an impressive work (Note: I have not studied this paper).

"... In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins. EquiBind is based on its predecessor, EquiDock ...
What’s more, 90 percent of all [newly developed] drugs fail once they are tested in humans due to having no effects or too many side effects. ..."

From the abstract:

"Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. ... Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization."

Artificial intelligence model finds potential drug molecules a thousand times faster | MIT News | Massachusetts Institute of Technology A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (open access. The senior author, i.e. Tommi Jaakkola, is a well known ML researcher with over 40,000 lifetime citations)