水准点(测量)
一般化
计算机科学
财产(哲学)
源代码
编码(集合论)
班级(哲学)
人工智能
功能(生物学)
任务(项目管理)
机器学习
自然语言处理
数据挖掘
程序设计语言
数学
哲学
认识论
数学分析
管理
大地测量学
集合(抽象数据类型)
进化生物学
经济
生物
地理
作者
Ruochi Zhang,Chao Wu,Qian Yang,Liu Chang,Yan Wang,Kewei Li,Lan Huang,Fengfeng Zhou
标识
DOI:10.1093/bioinformatics/btae118
摘要
Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models.This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery.We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.
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