Quantitative Analysis for Chinese and US-listed Pharmaceutical Companies by the LightGBM Algorithm.

IF 1.5 4区医学 Q4 CHEMISTRY, MEDICINAL

Current computer-aided drug design Pub Date : 2023-01-01 DOI:10.2174/1573409919666230126095901

Wenwen Zheng, Junjun Li, Yu Wang, Zhuyifan Ye, Hao Zhong, Hung Wan Kot, Defang Ouyang, Ging Chan

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引用次数: 0

Abstract

Aim: This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm.

Background: In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market.

Objective: To collect data from the database and apply machine learning to build the model.

Methods: LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies.

Results: The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries.

Conclusion: In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.

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LightGBM算法对中美上市医药公司的定量分析

目的:本文旨在通过机器学习算法定量分析美国和中国上市制药公司的增长趋势。背景:近二十年来，全球制药行业面临着研发成功率低的困境。美国是全球最大的医药市场，中国是最大的新兴市场。目的:从数据库中收集数据，应用机器学习技术建立模型。方法:采用LightGBM算法建立模型，识别影响制药企业绩效的重要因素。结果:美国公司的预测准确率为80.3%，中国公司的预测准确率为64.9%。特征重要性表明净利润增长率和负债负债率在财务指标中具有显著性。结果表明，美国可能继续主导全球制药行业，而几家中国制药公司在2015年后急剧上升，中美制药行业差距缩小。结论:综上所述，我们的研究通过机器学习算法定量分析了美国和中国上市制药公司的增长趋势，为全球制药行业提供了一个新的视角。根据研发能力和盈利能力，将141家美国上市制药公司和129家中国上市制药公司分为四个层次，对制药公司的成长趋势进行评价。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current computer-aided drug design 医学-计算机：跨学科应用

CiteScore

3.70

自引率

5.90%

发文量

审稿时长

>12 weeks

期刊介绍： Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.