Molecular conformation optimization is crucial to computer-aided drug
discovery and materials design. Traditional energy minimization techniques rely
on iterative optimization methods that use molecular forces calculated by a
physical simulator (oracle) as anti-gradients. However, this is a
computationally expensive approach that requires many interactions with a
physical simulator. One way to accelerate this procedure is to replace the
physical simulator with a neural network. Despite recent progress in neural
networks for molecular conformation energy prediction, such models are prone to
distribution shift, leading to inaccurate energy minimization. We find that the
quality of energy minimization with neural networks can be improved by
providing optimization trajectories as additional training data. Still, it
takes around $5 \times 10^5$ additional conformations to match the physical
simulator's optimization quality. In this work, we present the Gradual
Optimization Learning Framework (GOLF) for energy minimization with neural
networks that significantly reduces the required additional data. The framework
consists of an efficient data-collecting scheme and an external optimizer. The
external optimizer utilizes gradients from the energy prediction model to
generate optimization trajectories, and the data-collecting scheme selects
additional training data to be processed by the physical simulator. Our results
demonstrate that the neural network trained with GOLF performs on par with the
oracle on a benchmark of diverse drug-like molecules using $50$x less
additional data.

传统的能量最小化技术依赖于使用物理模拟器 (oracle) 计算的分子力作为反梯度的迭代优化方法，但这种计算密集的方法需要与物理模拟器进行多次交互。本文提出了 Gradual Optimization Learning Framework (GOLF)，通过使用神经网络生成优化轨迹并选择额外的训练数据，显著降低了所需的额外数据量，实现了与基准药物类分子相媲美的性能。

逐步优化学习用于构象能量最小化

Gradual Optimization Learning for Conformational Energy Minimization

Predicting the bioactivity of a ligand is one of the hardest and most
important challenges in computer-aided drug discovery. Despite years of data
collection and curation efforts by research organizations worldwide,
bioactivity data remains sparse and heterogeneous, thus hampering efforts to
build predictive models that are accurate, transferable and robust. The
intrinsic variability of the experimental data is further compounded by data
aggregation practices that neglect heterogeneity to overcome sparsity. Here we
discuss the limitations of these practices and present a hierarchical
meta-learning framework that exploits the information synergy across disparate
assays by successfully accounting for assay heterogeneity. We show that the
model achieves a drastic improvement in affinity prediction across diverse
protein targets and assay types compared to conventional baselines. It can
quickly adapt to new target contexts using very few observations, thus enabling
large-scale virtual screening in early-phase drug discovery.

模型利用异质性药效数据的信息协同，成功应对了试剂异质性，极大地提高了不同蛋白质靶点和试剂类型的亲和力预测，从而在早期药物发现中实现了大规模虚拟筛选。