Given the time and expense associated with bringing a drug to market,
numerous studies have been conducted to predict the properties of compounds
based on their structure using machine learning. Federated learning has been
applied to compound datasets to increase their prediction accuracy while
safeguarding potentially proprietary information. However, federated learning
is encumbered by low accuracy in not identically and independently distributed
(non-IID) settings, i.e., data partitioning has a large label bias, and is
considered unsuitable for compound datasets, which tend to have large label
bias. To address this limitation, we utilized an alternative method of
distributed machine learning to chemical compound data from open sources,
called data collaboration analysis (DC). We also proposed data collaboration
analysis using projection data (DCPd), which is an improved method that
utilizes auxiliary PubChem data. This improves the quality of individual
user-side data transformations for the projection data for the creation of
intermediate representations. The classification accuracy, i.e., area under the
curve in the receiver operating characteristic curve (ROC-AUC) and AUC in the
precision-recall curve (PR-AUC), of federated averaging (FedAvg), DC, and DCPd
was compared for five compound datasets. We determined that the machine
learning performance for non-IID settings was in the order of DCPd, DC, and
FedAvg, although they were almost the same in identically and independently
distributed (IID) settings. Moreover, the results showed that compared to other
methods, DCPd exhibited a negligible decline in classification accuracy in
experiments with different degrees of label bias. Thus, DCPd can address the
low performance in non-IID settings, which is one of the challenges of
federated learning.

使用分布式机器学习方法，通过数据协同分析和投影数据，改进了联邦学习系统，提高了药物预测的准确性和分类性能。

应用于化合物数据集的数据协同分析及投影数据在非独立同分布场景的引入

Data Collaboration Analysis applied to Compound Datasets and the  Introduction of Projection data to Non-IID settings

G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell
transmembrane proteins, responsible for numerous biological processes. From a
practical viewpoint around 34\% of the drugs approved by the US Food and Drug
Administration target these receptors. They can be analyzed from their
simulated molecular dynamics, including the prediction of their behavior in the
presence of drugs. In this paper, the capability of Long Short-Term Memory
Networks (LSTMs) are evaluated to learn and predict the molecular dynamic
trajectories of a receptor. Several models were trained with the 3D position of
the amino acids of the receptor considering different transformations on the
position of the amino acid, such as their centers of mass, the geometric
centers and the position of the $\alpha$--carbon for each amino acid. The error
of the prediction of the position was evaluated by the mean average error (MAE)
and root-mean-square deviation (RMSD). The LSTM models show a robust
performance, with results comparable to the state-of-the-art in non-dynamic 3D
predictions. The best MAE and RMSD values were found for the mass center of the
amino acids with 0.078 Å and 0.156 Å respectively. This work shows the
potential of LSTM to predict the molecular dynamics of GPRCs.

本研究评估了长短时记忆网络 （LSTM）学习和预测受体的分子动态轨迹的能力。实验表明，该方法在分析 G 蛋白偶联受体分子动态时表现出强大的性能，可用于药物预测及蛋白质结构分析等领域。