Well-designed molecular representations (fingerprints) are vital to combine
medical chemistry and deep learning. Whereas incorporating 3D geometry of
molecules (i.e. conformations) in their representations seems beneficial,
current 3D algorithms are still in infancy. In this paper, we propose a novel
molecular representation algorithm which preserves 3D conformations of
molecules with a Molecular Hamiltonian Network (HamNet). In HamNet, implicit
positions and momentums of atoms in a molecule interact in the Hamiltonian
Engine following the discretized Hamiltonian equations. These implicit
coordinations are supervised with real conformations with translation- &
rotation-invariant losses, and further used as inputs to the Fingerprint
Generator, a message-passing neural network. Experiments show that the
Hamiltonian Engine can well preserve molecular conformations, and that the
fingerprints generated by HamNet achieve state-of-the-art performances on
MoleculeNet, a standard molecular machine learning benchmark.

该论文提出了一种新的分子表示算法 —— 分子哈密顿网络（HamNet），可以保存分子的 3D 构象，并且表现优异，可以应用在标准的分子机器学习基准测试 MoleculeNet 上。

HamNet: 基于哈密顿神经网络的构象引导分子表示

HamNet: Conformation-Guided Molecular Representation with Hamiltonian  Neural Networks

Molecular machine learning has been maturing rapidly over the last few years.
Improved methods and the presence of larger datasets have enabled machine
learning algorithms to make increasingly accurate predictions about molecular
properties. However, algorithmic progress has been limited due to the lack of a
standard benchmark to compare the efficacy of proposed methods; most new
algorithms are benchmarked on different datasets making it challenging to gauge
the quality of proposed methods. This work introduces MoleculeNet, a large
scale benchmark for molecular machine learning. MoleculeNet curates multiple
public datasets, establishes metrics for evaluation, and offers high quality
open-source implementations of multiple previously proposed molecular
featurization and learning algorithms (released as part of the DeepChem open
source library). MoleculeNet benchmarks demonstrate that learnable
representations are powerful tools for molecular machine learning and broadly
offer the best performance. However, this result comes with caveats. Learnable
representations still struggle to deal with complex tasks under data scarcity
and highly imbalanced classification. For quantum mechanical and biophysical
datasets, the use of physics-aware featurizations can be more important than
choice of particular learning algorithm.

MoleculeNet 是一个大规模分子机器学习基准测试，为多种先前提出的分子特征化和学习算法提供高质量的开源实现，并证明了可学习的表示是分子机器学习的强大工具，但其性能受到数据稀缺和高度不平衡分类的影响，物理感知特征化的使用对于量子力学和生物物理数据集更为重要。