Conformation Generation is a fundamental problem in drug discovery and
cheminformatics. And organic molecule conformation generation, particularly in
vacuum and protein pocket environments, is most relevant to drug design.
Recently, with the development of geometric neural networks, the data-driven
schemes have been successfully applied in this field, both for molecular
conformation generation (in vacuum) and binding pose generation (in protein
pocket). The former beats the traditional ETKDG method, while the latter
achieves similar accuracy compared with the widely used molecular docking
software. Although these methods have shown promising results, some researchers
have recently questioned whether deep learning (DL) methods perform better in
molecular conformation generation via a parameter-free method. To our surprise,
what they have designed is some kind analogous to the famous infinite monkey
theorem, the monkeys that are even equipped with physics education. To discuss
the feasibility of their proving, we constructed a real infinite stochastic
monkey for molecular conformation generation, showing that even with a more
stochastic sampler for geometry generation, the coverage of the benchmark
QM-computed conformations are higher than those of most DL-based methods. By
extending their physical monkey algorithm for binding pose prediction, we also
discover that the successful docking rate also achieves near-best performance
among existing DL-based docking models. Thus, though their conclusions are
right, their proof process needs more concern.

本文探讨了利用深度学习和基于物理学原理的两种方法进行分子构象生成和蛋白质配体定位的可行性，提出基于无穷随机猴定理的分子构象生成方法，证明了其覆盖率比大多数深度学习方法更高，同时也发现了其在蛋白质配体定位方面的成功率接近最佳表现。

无限物理猴：深度学习方法在构象生成中真的表现更好吗？

Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better  in Conformation Generation?

We study how to generate molecule conformations (i.e., 3D structures) from a
molecular graph. Traditional methods, such as molecular dynamics, sample
conformations via computationally expensive simulations. Recently, machine
learning methods have shown great potential by training on a large collection
of conformation data. Challenges arise from the limited model capacity for
capturing complex distributions of conformations and the difficulty in modeling
long-range dependencies between atoms. Inspired by the recent progress in deep
generative models, in this paper, we propose a novel probabilistic framework to
generate valid and diverse conformations given a molecular graph. We propose a
method combining the advantages of both flow-based and energy-based models,
enjoying: (1) a high model capacity to estimate the multimodal conformation
distribution; (2) explicitly capturing the complex long-range dependencies
between atoms in the observation space. Extensive experiments demonstrate the
superior performance of the proposed method on several benchmarks, including
conformation generation and distance modeling tasks, with a significant
improvement over existing generative models for molecular conformation
sampling.

本文提出了一种结合了流模型与能量模型的方法，以解决机器学习在从分子图生成分子空间构象时存在的挑战。经过大量实验，证明该方法在分子构象生成和距离建模任务上具有显著的改进和卓越性能。