Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17644-17655, 2022.

Abstract

Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown 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 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 drug-likeness and synthetic accessibility.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-peng22b, title = {{P}ocket2{M}ol: Efficient Molecular Sampling Based on 3{D} Protein Pockets}, author = {Peng, Xingang and Luo, Shitong and Guan, Jiaqi and Xie, Qi and Peng, Jian and Ma, Jianzhu}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {17644--17655}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/peng22b/peng22b.pdf}, url = {https://proceedings.mlr.press/v162/peng22b.html}, abstract = {Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown 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 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 drug-likeness and synthetic accessibility.} }
Endnote
%0 Conference Paper %T Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets %A Xingang Peng %A Shitong Luo %A Jiaqi Guan %A Qi Xie %A Jian Peng %A Jianzhu Ma %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-peng22b %I PMLR %P 17644--17655 %U https://proceedings.mlr.press/v162/peng22b.html %V 162 %X Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown 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 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 drug-likeness and synthetic accessibility.
APA
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J. & Ma, J.. (2022). Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:17644-17655 Available from https://proceedings.mlr.press/v162/peng22b.html.

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