Deep generative models have achieved tremendous success in designing novel
drug molecules in recent years. A new thread of works have shown the great
potential in advancing the specificity and success rate of in silico drug
design by considering the structure of protein pockets. This setting posts
fundamental computational challenges in sampling new chemical compounds that
could satisfy multiple geometrical constraints imposed by pockets. Previous
sampling algorithms either sample in the graph space or only consider the 3D
coordinates of atoms while ignoring other detailed chemical structures such as
bond types and functional groups. To address the challenge, we develop
Pocket2Mol, an E(3)-equivariant generative network composed of two modules: 1)
a new graph neural network capturing both spatial and bonding relationships
between atoms of the binding pockets and 2) a new efficient algorithm which
samples new drug candidates conditioned on the pocket representations from a
tractable distribution without relying on MCMC. Experimental results
demonstrate that molecules sampled from Pocket2Mol achieve significantly better
binding affinity and other drug properties such as druglikeness and synthetic
accessibility.

Pocket2Mol 是一个用于 in silico 药物设计的 E (3)- 等变生成网络，它能采样新的受约束化合物，考虑了蛋白质袋的结构。从 Pocket2Mol 中采样的分子具有显著更好的结合亲和力和其他药物性质。