ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling

Most molecular generative models based on artificial intelligence for de novo drug design are ligand-centric and do not consider the detailed three-dimensional geometries of protein binding pockets. Pocket-aware three-dimensional molecular generation is challenging due to the need to impose physical equivariance and to evaluate protein–ligand interactions when incrementally growing partially built molecules. Inspired by multiscale modelling in condensed matter and statistical physics, we present a three-dimensional molecular generative model conditioned on protein pockets, termed ResGen, for designing organic molecules inside of a given target. ResGen is built on the principle of parallel multiscale modelling, which can capture higher-level interaction and achieve higher computational efficiency (about eight-times faster than the previous best art). The generation process is formulated as a hierarchical autoregression, that is, a global autoregression for learning protein–ligand interactions and atomic component autoregression for learning each atom’s topology and geometry distributions. We demonstrate that ResGen has a higher success rate than existing state-of-the-art approaches in generating novel molecules that can bind to unseen targets more tightly than the original ligands. Moreover, retrospective computational experiments on de novo drug design in real-world scenarios show that ResGen successfully generates drug-like molecules with lower binding energy and higher diversity than state-of-the-art approaches.