A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

计算机科学 机器学习 人工智能 高斯过程 药物输送 算法 高斯分布 纳米技术 材料科学 化学 计算化学
作者
Labiba Noorain,Vu Nguyen,Hae‐Won Kim,Nguyen Thuy Ba Linh
出处
期刊:Pharmaceutics [MDPI AG]
卷期号:15 (2): 495-495 被引量:14
标识
DOI:10.3390/pharmaceutics15020495
摘要

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI

祝大家在新的一年里科研腾飞
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
wangyr11发布了新的文献求助10
1秒前
2秒前
3秒前
科研通AI2S应助零慧采纳,获得10
4秒前
5秒前
一二完成签到,获得积分10
7秒前
蜡笔小猪完成签到,获得积分10
7秒前
流风回雪完成签到,获得积分10
9秒前
9秒前
10秒前
10秒前
11秒前
蜡笔小猪发布了新的文献求助10
12秒前
思源应助dmsoli采纳,获得30
12秒前
衣裳薄发布了新的文献求助10
14秒前
Mansis发布了新的文献求助10
14秒前
黄10086发布了新的文献求助10
15秒前
16秒前
16秒前
共享精神应助李哥采纳,获得20
19秒前
小李在哪儿完成签到 ,获得积分10
19秒前
科研通AI2S应助西西采纳,获得10
19秒前
20秒前
慧海拾穗完成签到 ,获得积分10
21秒前
22秒前
24秒前
24秒前
酷波er应助sisiguai采纳,获得10
24秒前
1ssd应助派大星采纳,获得10
26秒前
无名老大应助光光发电采纳,获得20
26秒前
珂千山发布了新的文献求助10
27秒前
任伟超发布了新的文献求助10
28秒前
徐妙菱发布了新的文献求助10
29秒前
30秒前
我要发Nture完成签到,获得积分10
33秒前
33秒前
晓晓发布了新的文献求助10
35秒前
聆风完成签到,获得积分10
36秒前
HEIKU应助zjh采纳,获得10
36秒前
我是老大应助吴所畏惧采纳,获得10
37秒前
高分求助中
Востребованный временем 2500
Les Mantodea de Guyane 1000
Very-high-order BVD Schemes Using β-variable THINC Method 970
Field Guide to Insects of South Africa 660
Foucault's Technologies Another Way of Cutting Reality 500
Forensic Chemistry 400
Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia 300
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3392477
求助须知:如何正确求助?哪些是违规求助? 3003086
关于积分的说明 8807533
捐赠科研通 2689819
什么是DOI,文献DOI怎么找? 1473318
科研通“疑难数据库(出版商)”最低求助积分说明 681547
邀请新用户注册赠送积分活动 674351