Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

By Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma. International Conference on Machine Learning, 2022

333 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ecd7332cd3d34220a8cf01c30385da123c61dff0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Bruno E. Correia
2. Jianzhu Ma
3. Jian Peng
4. Bin Cui
5. Max Welling
6. Ming-Hsuan Yang
7. Michael M. Bronstein
8. Jiaqi Guan

In Materials Chemistry

1. Jianzhu Ma
2. Yuanqi Du
3. Jian Peng
4. Xingang Peng
5. Bruno E. Correia
6. Jiaqi Guan
7. Yufeng Su
8. Wesley Wei Qian

In Molecular Biology

1. Jianzhu Ma
2. Jinbo Xu
3. Bruno E. Correia
4. Jian Peng
5. Bin Cui
6. Wesley Wei Qian
7. Max Welling
8. Ming-Hsuan Yang

Citations in the corpus

• Structure-based drug design with equivariant diffusion models (2022, positive): “We use the CrossDocked dataset [26] with 100,000 high-quality protein-ligand pairs for training and 100 proteins for testing, following previous works [15, 60].”
• Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “The model can compete with previous autoregressive generation models (such as Pocket2Mol [204]) given some target proteins, demonstrating its superior performance.”
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “In terms of wall clock time, AR, Pocket2Mol and GraphBP use 7785s, 2544s and 105s for generating 100 valid molecules on average separately, and it takes 3428s on average for TargetDiff.”