Artificial intelligence in the life sciences最新文献_第8页

Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model? 抗菌肽预测的耦合编码方法：高度准确的模型有多敏感？

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100034

Ivan Erjavac , Daniela Kalafatovic , Goran Mauša

{"title":"Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?","authors":"Ivan Erjavac , Daniela Kalafatovic , Goran Mauša","doi":"10.1016/j.ailsci.2022.100034","DOIUrl":"10.1016/j.ailsci.2022.100034","url":null,"abstract":"<div><p>Current application of machine learning in the process of antimicrobial peptide discovery call for the reduction of the false positive predictions that are produced by the classification models. Considering that the positive predictions of high confidence drive modern experimental design, the model’s sensitivity is crucial to reduce the number of unnecessary <em>in vitro</em> tests. Furthermore, taking into account the expert-based design approaches that employ random mutations on confirmed sequences, the machine learning models are required to distinguish between subtle differences among shuffled sequences. With the goal of reducing the false positive rate and improving sensitivity, we propose a hybrid approach to antimicrobial peptide prediction that utilizes combined encoding models. To this end, we implement models that employ both the physico-chemical features and sequence ordering information to stress the importance of using both representations. We also investigate the usage of binary encoding for peptide representation purposes, a method that is insufficiently represented in related research, which proved to act as a viable low dimensional alternative to the one-hot encoding. Our results, supported by Cochran and McNemar statistical tests and Spearman correlation analysis, indicate that the sequence-based encodings complement the physico-chemical features and their synergic effect yields improvement in terms of every evaluation metric. Finally, the proposed hybrid approach that combines physico-chemical features and binary encoding using logical conjunction was shown to be superior to other single models by a factor of 2.96 in terms of fall-out and up to 6.1% in terms of precision.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000058/pdfft?md5=3f5cf3ee0ab97ece8587283b98a0d00f&pid=1-s2.0-S2667318522000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49610236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods 基于机器学习方法的JAK1抑制剂分类及SAR研究

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100039

Zhenwu Yang , Yujia Tian , Yue Kong , Yushan Zhu , Aixia Yan

{"title":"Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods","authors":"Zhenwu Yang , Yujia Tian , Yue Kong , Yushan Zhu , Aixia Yan","doi":"10.1016/j.ailsci.2022.100039","DOIUrl":"https://doi.org/10.1016/j.ailsci.2022.100039","url":null,"abstract":"<div><p>Janus kinase 1 (JAK1) is a key regulator of gene transcription, inhibition of JAK1 is an intervention for many diseases including rheumatoid arthritis and Crohn's disease. In this study, we collected a dataset containing 2982 JAK1 inhibitors, characterized molecules by MACCS fingerprints and Morgan fingerprints. We used support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting tree (XGBoost) algorithms to build 16 traditional machine learning classification models. Additionally, we utilized deep neural networks (DNN) to develop four deep learning models. The best model (Model 3B) built by RF and Morgan fingerprints achieved the accuracy (ACC) of 93.6% and Mathews correlation coefficient (MCC) of 0.87 on the test set. Furthermore, we made structure–activity relationship (SAR) analyses for JAK1 inhibitors, based on the output from the random forest models. After analyzing the important keys of two types of fingerprints, it was observed that some substructures such as pyrazole, pyrrolotriazolopyrimidine and pyrazolopyrimidine appeared frequently in highly active JAK1 inhibitors.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000101/pdfft?md5=2754446c7965603153a27ece060160a4&pid=1-s2.0-S2667318522000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91728648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SyntaLinker-Hybrid: A deep learning approach for target specific drug design syntalink - hybrid:一种针对特定靶标药物设计的深度学习方法

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100035

Yu Feng , Yuyao Yang , Wenbin Deng , Hongming Chen , Ting Ran

引用次数: 0

An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis 一个无监督的计算管道识别潜在的可重复利用的药物治疗亨廷顿氏病和多发性硬化症

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100042

Luca Menestrina, Maurizio Recanatini

{"title":"An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis","authors":"Luca Menestrina, Maurizio Recanatini","doi":"10.1016/j.ailsci.2022.100042","DOIUrl":"10.1016/j.ailsci.2022.100042","url":null,"abstract":"<div><p>Drug repurposing consists in identifying additional uses for known drugs and, since these new findings are built on previous knowledge, it reduces both the length and the costs of the drug development. In this work, we assembled an automated computational pipeline for drug repurposing, integrating also a network-based analysis for screening the possible drug combinations. The selection of drugs relies both on their proximity to the disease on the protein-protein interactome and on their influence on the expression of disease-related genes. Combined therapies are then prioritized on the basis of the drugs’ separation on the human interactome and the known drug-drug interactions. We eventually collected a number of molecules, and their plausible combinations, that could be proposed for the treatment of Huntington's disease and multiple sclerosis. Finally, this pipeline could potentially provide new suggestions also for other complex disorders.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000125/pdfft?md5=02a08224e3d5097be5747fc8a22c3572&pid=1-s2.0-S2667318522000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42636492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LIDeB Tools: A Latin American resource of freely available, open-source cheminformatics apps LIDeB Tools:拉丁美洲免费提供的开源化学信息学应用程序资源

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100049

Denis N. Prada Gori, Lucas N. Alberca, Santiago Rodriguez, Juan I. Alice, Manuel A. Llanos, Carolina L. Bellera, Alan Talevi

{"title":"LIDeB Tools: A Latin American resource of freely available, open-source cheminformatics apps","authors":"Denis N. Prada Gori, Lucas N. Alberca, Santiago Rodriguez, Juan I. Alice, Manuel A. Llanos, Carolina L. Bellera, Alan Talevi","doi":"10.1016/j.ailsci.2022.100049","DOIUrl":"10.1016/j.ailsci.2022.100049","url":null,"abstract":"<div><p>Cheminformatics is the chemical field that deals with the storage, retrieval, analysis and manipulation of an increasing volume of available chemical data, and it plays a fundamental role in the fields of drug discovery, biology, chemistry, and biochemistry. Open source and freely available cheminformatics tools not only contribute to the generation of public knowledge, but also to reduce the technological gap between high- and low- to middle-income countries. Here, we describe a series of in-house cheminformatics applications developed by our academic drug discovery team, which are freely available on our website (<span>https://lideb.biol.unlp.edu.ar/</span><svg><path></path></svg>) as Web Apps and stand-alone versions. These apps include tools for clustering small molecules, decoy generation, druggability assessment, classificatory model evaluation, and data standardization and visualization.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100049"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000198/pdfft?md5=022e4e88e07795a9a57aee98fede7162&pid=1-s2.0-S2667318522000198-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47979278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Revisiting active learning in drug discovery through open science 通过开放科学重新审视药物发现中的主动学习

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100051

Jürgen Bajorath

引用次数: 0

Recent advances and application of generative adversarial networks in drug discovery, development, and targeting 生成对抗网络在药物发现、开发和靶向中的最新进展和应用

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100045

Satvik Tripathi , Alisha Isabelle Augustin , Adam Dunlop , Rithvik Sukumaran , Suhani Dheer , Alex Zavalny , Owen Haslam , Thomas Austin , Jacob Donchez , Pushpendra Kumar Tripathi , Edward Kim

{"title":"Recent advances and application of generative adversarial networks in drug discovery, development, and targeting","authors":"Satvik Tripathi , Alisha Isabelle Augustin , Adam Dunlop , Rithvik Sukumaran , Suhani Dheer , Alex Zavalny , Owen Haslam , Thomas Austin , Jacob Donchez , Pushpendra Kumar Tripathi , Edward Kim","doi":"10.1016/j.ailsci.2022.100045","DOIUrl":"10.1016/j.ailsci.2022.100045","url":null,"abstract":"<div><p>A rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation of therapeutically relevant medications for the treatment of a variety of ailments. In this review, we focus on the most recent advances in the areas of drug design and discovery research employing generative deep learning methodologies such as generative adversarial network (GAN) frameworks. To begin, we examine drug design and discovery studies that use several GAN methodologies to evaluate one key application, such as molecular <em>de novo</em> design in drug design and discovery. Furthermore, we discuss many GAN models for dimension reduction of single-cell data at the preclinical stage of the drug development pipeline. We also show various experiments in <em>de novo</em> peptide and protein creation utilizing GAN frameworks. Furthermore, we discuss the limits of past drug design and discovery research employing GAN models. Finally, we give a discussion on future research prospects and obstacles.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000150/pdfft?md5=9c33e9c2ba0eb38e17020fefccff7451&pid=1-s2.0-S2667318522000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43912790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

AI in Life Science Research – The Road Ahead 生命科学研究中的人工智能-未来之路

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100030

Jürgen Bajorath

引用次数: 0

Open protocols for docking and MD-based scoring of peptide substrates 肽底物对接和基于MD评分的开放协议

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100044

Rodrigo Ochoa , Ángel Santiago , Melissa Alegría-Arcos

{"title":"Open protocols for docking and MD-based scoring of peptide substrates","authors":"Rodrigo Ochoa , Ángel Santiago , Melissa Alegría-Arcos","doi":"10.1016/j.ailsci.2022.100044","DOIUrl":"10.1016/j.ailsci.2022.100044","url":null,"abstract":"<div><p>The study of protein-peptide interactions is an active research field from an experimental and computational perspective, with the latest presenting challenges to model and simulate the peptides' intrinsic flexibility. Predicting affinities towards protein systems of interest, such as proteases, is crucial to understand the specificity of the interactions and support the discovery of novel substrates. Here we provide a set of computational protocols to run structural and dynamical analysis of protein-peptide complexes from a binding perspective. The protocols are based on state-of-the-art methods, but the code is open and can be customized depending on the user needs. These include a fragment-growing peptide docking protocol to predict bound conformations of flexible peptides, a protocol to extract descriptors from protein-peptide molecular dynamics trajectories, and a workflow to build and test machine learning regression models. As a toy example, we applied the protocols to a serine protease structure with a set of known peptide substrates and random sequences to illustrate the use of the code, which is publicly available at: <span>https://github.com/rochoa85/Protocols-Peptide-Binding</span><svg><path></path></svg></p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000149/pdfft?md5=37f48baa6e0b2e91691325276818a26d&pid=1-s2.0-S2667318522000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41545827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The commoditization of AI for molecule design 人工智能在分子设计中的商品化

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100031

Fabio Urbina, Sean Ekins

{"title":"The commoditization of AI for molecule design","authors":"Fabio Urbina, Sean Ekins","doi":"10.1016/j.ailsci.2022.100031","DOIUrl":"10.1016/j.ailsci.2022.100031","url":null,"abstract":"<div><p>Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become “designed by AI”. AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for <em>de novo</em> design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10653331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5