Opioid Use Disorder (OUD) has emerged as a significant global public health
issue, with complex multifaceted conditions. Due to the lack of effective
treatment options for various conditions, there is a pressing need for the
discovery of new medications. In this study, we propose a deep generative model
that combines a stochastic differential equation (SDE)-based diffusion modeling
with the latent space of a pretrained autoencoder model. The molecular
generator enables efficient generation of molecules that are effective on
multiple targets, specifically the mu, kappa, and delta opioid receptors.
Furthermore, we assess the ADMET (absorption, distribution, metabolism,
excretion, and toxicity) properties of the generated molecules to identify
drug-like compounds. To enhance the pharmacokinetic properties of some lead
compounds, we employ a molecular optimization approach. We obtain a diverse set
of drug-like molecules. We construct binding affinity predictors by integrating
molecular fingerprints derived from autoencoder embeddings, transformer
embeddings, and topological Laplacians with advanced machine learning
algorithms. Further experimental studies are needed to evaluate the
pharmacological effects of these drug-like compounds for OUD treatment. Our
machine learning platform serves as a valuable tool in designing and optimizing
effective molecules for addressing OUD.

本研究提出了一种深度生成模型，利用自编码器模型的潜在空间，结合基于随机微分方程的扩散建模，实现有效生成对多种目标，包括 mu、kappa 和 delta 阿片类受体有效的分子，通过整合自动编码器嵌入分子指纹、变压器嵌入和拓扑拉普拉斯与先进机器学习算法构建亲和力预测器，进一步需进行实验评估，以评估这些类药物化合物在 OUD 治疗中的药理效应，该机器学习平台可作为设计和优化针对 OUD 的有效分子的有价值工具。

多目标分子优化在利用生成网络复合物治疗阿片类药物使用障碍中的应用

Multi-objective Molecular Optimization for Opioid Use Disorder Treatment  Using Generative Network Complex

The identification of key factors such as medications, diseases, and
relationships within electronic health records and clinical notes has a wide
range of applications in the clinical field. In the N2C2 2022 competitions,
various tasks were presented to promote the identification of key factors in
electronic health records (EHRs) using the Contextualized Medication Event
Dataset (CMED). Pretrained large language models (LLMs) demonstrated
exceptional performance in these tasks. This study aims to explore the
utilization of LLMs, specifically ChatGPT, for data augmentation to overcome
the limited availability of annotated data for identifying the key factors in
EHRs. Additionally, different pre-trained BERT models, initially trained on
extensive datasets like Wikipedia and MIMIC, were employed to develop models
for identifying these key variables in EHRs through fine-tuning on augmented
datasets. The experimental results of two EHR analysis tasks, namely medication
identification and medication event classification, indicate that data
augmentation based on ChatGPT proves beneficial in improving performance for
both medication identification and medication event classification.

本研究旨在探讨利用预训练的大型语言模型 (ChatGPT) 进行数据增强来识别电子病历中的关键因素，结果表明基于 ChatGPT 的数据增强对药物识别和药物事件分类任务的性能提高有益。