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。