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Exploring chemical space — Generative models and their evaluation 探索化学空间——生成模型及其评价
Artificial intelligence in the life sciences Pub Date : 2023-02-04 DOI: 10.1016/j.ailsci.2023.100064
Martin Vogt
{"title":"Exploring chemical space — Generative models and their evaluation","authors":"Martin Vogt","doi":"10.1016/j.ailsci.2023.100064","DOIUrl":"10.1016/j.ailsci.2023.100064","url":null,"abstract":"<div><p>Recent advances in the field of artificial intelligence, specifically regarding deep learning methods, have invigorated research into novel ways for the exploration of chemical space. Compared to more traditional methods that rely on chemical fragments and combinatorial recombination deep generative models generate molecules in a non-transparent way that defies easy rationalization. However, this opaque nature also promises to explore uncharted chemical space in novel ways that do not rely on structural similarity directly. These aspects and the complexity of training such models makes model assessment regarding novelty, uniqueness, and distribution of generated molecules a central aspect. This perspective gives an overview of current methodologies for chemical space exploration with an emphasis on deep neural network approaches. Key aspects of generative models include choice of molecular representation, the targeted chemical space, and the methodology for assessing and validating chemical space coverage.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100064"},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48370934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil 机器学习用于巴西圣保罗恶性肿瘤患者纵向死亡率风险预测
Artificial intelligence in the life sciences Pub Date : 2023-02-03 DOI: 10.1016/j.ailsci.2023.100061
GFS Silva , LS Duarte , MM Shirassu , SV Peres , MA de Moraes , A Chiavegatto Filho
{"title":"Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil","authors":"GFS Silva ,&nbsp;LS Duarte ,&nbsp;MM Shirassu ,&nbsp;SV Peres ,&nbsp;MA de Moraes ,&nbsp;A Chiavegatto Filho","doi":"10.1016/j.ailsci.2023.100061","DOIUrl":"10.1016/j.ailsci.2023.100061","url":null,"abstract":"<div><p>Artificial intelligence is becoming an important diagnostic and prognostic tool in recent years, as machine learning algorithms have been shown to improve clinical decision-making. These algorithms will have some of their most important applications in developing regions with restricted data collection, but their performance under this condition is still widely unknown. We analyzed longitudinal data from São Paulo, Brazil, to develop machine learning algorithms to predict the risk of death in patients with cancer. We tested different algorithms using nine separate model structures. Considering the area under the ROC curve (AUC-ROC), we obtained values of 0.946 for the general model, 0.945 for the model with the five main cancers, 0.899 for bronchial and lung cancer, 0.947 for breast cancer, 0.866 for stomach cancer, 0.872 for colon cancer, 0.923 for rectum cancer, 0.955 for prostate cancer, and 0.917 for uterine cervix cancer. Our results indicate the potential of building models for predicting mortality risk in cancer patients in developing regions using only routinely-collected data.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49483214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Deep graph learning in molecular docking: Advances and opportunities 分子对接中的深度图学习:进展与机遇
Artificial intelligence in the life sciences Pub Date : 2023-02-03 DOI: 10.1016/j.ailsci.2023.100062
Norberto Sánchez-Cruz
{"title":"Deep graph learning in molecular docking: Advances and opportunities","authors":"Norberto Sánchez-Cruz","doi":"10.1016/j.ailsci.2023.100062","DOIUrl":"10.1016/j.ailsci.2023.100062","url":null,"abstract":"<div><p>One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48198299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Using ontologies for life science text-based resource organization 本体论在生命科学中的应用基于文本的资源组织
Artificial intelligence in the life sciences Pub Date : 2023-01-27 DOI: 10.1016/j.ailsci.2023.100059
Giulia Panzarella , Pierangelo Veltri , Stefano Alcaro
{"title":"Using ontologies for life science text-based resource organization","authors":"Giulia Panzarella ,&nbsp;Pierangelo Veltri ,&nbsp;Stefano Alcaro","doi":"10.1016/j.ailsci.2023.100059","DOIUrl":"10.1016/j.ailsci.2023.100059","url":null,"abstract":"<div><p>Ontologies are used to support access to a multitude of databases that cover domains relevant information. Heterogeneity and different semantics can be accessed by using structured texts and descriptions in a hierarchical concept definition. We are interested in Life Sciences (LS) related ontologies including components taken from molecular biology, bioinformatics, physics, chemistry, medicine and other related areas. An Ontology comprises: (i) term connections, (ii) the identification of core concepts, (iii) data management, (iv) knowledge classification and integration to collect key information. An ontology may be very useful in navigating through LS terms. This paper explores some available biomedical ontologies and frameworks. It describes the most common ontology development environments (ODE): Protégé, Topbraid Composer, Ontostudio, Fluent Editor, VocBench, Swoop and Obo-edit, to create ontologies from textual scientific resources for LS plans. It also compares ontology methodologies in terms of Usability, Scalability, Stability, Integration, Documentation and Originality.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42165958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Discovery of novel A2AR antagonists through deep learning-based virtual screening 通过基于深度学习的虚拟筛选发现新型A2AR拮抗剂
Artificial intelligence in the life sciences Pub Date : 2023-01-20 DOI: 10.1016/j.ailsci.2023.100058
Miru Tang , Chang Wen , Jie Lin , Hongming Chen , Ting Ran
{"title":"Discovery of novel A2AR antagonists through deep learning-based virtual screening","authors":"Miru Tang ,&nbsp;Chang Wen ,&nbsp;Jie Lin ,&nbsp;Hongming Chen ,&nbsp;Ting Ran","doi":"10.1016/j.ailsci.2023.100058","DOIUrl":"10.1016/j.ailsci.2023.100058","url":null,"abstract":"<div><p>The A<sub>2A</sub> adenosine receptor (A<sub>2A</sub>R) is emerging as a promising drug target for cancer immunotherapy. Novel A<sub>2A</sub>R antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A<sub>2A</sub>R antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A<sub>2A</sub>R antagonists for an extremely large molecular library were found successfully.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46817392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Application of AI techniques and robotics in agriculture: A review 人工智能技术和机器人技术在农业中的应用综述
Artificial intelligence in the life sciences Pub Date : 2023-01-06 DOI: 10.1016/j.ailsci.2023.100057
Manas Wakchaure , B.K. Patle , A.K. Mahindrakar
{"title":"Application of AI techniques and robotics in agriculture: A review","authors":"Manas Wakchaure ,&nbsp;B.K. Patle ,&nbsp;A.K. Mahindrakar","doi":"10.1016/j.ailsci.2023.100057","DOIUrl":"10.1016/j.ailsci.2023.100057","url":null,"abstract":"<div><p>The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), artificial potential field (APF), simulated annealing (SA), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search algorithm (HS), bat algorithm (BA), cell decomposition (CD) and firefly algorithm (FA)) in agriculture, focusing on expert systems, robots developed for agriculture, sensors technology for collecting and transmitting data, in an attempt to reveal their potential impact in the field of agriculture. None of the literature highlights the application of AI techniques and robots in (Cultivation, Monitoring, and Harvesting) to understand their contribution to the agriculture sector and the simultaneous comparison of each based on its usefulness and popularity. This work investigates the comparative analysis of three essential phases of agriculture: Cultivation, Monitoring, and Harvesting, by knowing the depth of AI involved and the robots utilized. The current study presents a systematic review of more than 150 papers based on the existing automation application in agriculture from 1960 to 2021. It highlights the future research gap in making intelligent autonomous systems in agriculture. The paper concludes with tabular data and charts comparing the frequency of individual AI approaches for specific applications in the agriculture field.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Machine learning for small molecule drug discovery in academia and industry 学术界和工业界用于小分子药物发现的机器学习
Artificial intelligence in the life sciences Pub Date : 2023-01-05 DOI: 10.1016/j.ailsci.2022.100056
Andrea Volkamer , Sereina Riniker , Eva Nittinger , Jessica Lanini , Francesca Grisoni , Emma Evertsson , Raquel Rodríguez-Pérez , Nadine Schneider
{"title":"Machine learning for small molecule drug discovery in academia and industry","authors":"Andrea Volkamer ,&nbsp;Sereina Riniker ,&nbsp;Eva Nittinger ,&nbsp;Jessica Lanini ,&nbsp;Francesca Grisoni ,&nbsp;Emma Evertsson ,&nbsp;Raquel Rodríguez-Pérez ,&nbsp;Nadine Schneider","doi":"10.1016/j.ailsci.2022.100056","DOIUrl":"10.1016/j.ailsci.2022.100056","url":null,"abstract":"<div><p>Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47508788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs 一个用于高效更新高度策划的病理生理机制知识图的自然语言处理系统
Artificial intelligence in the life sciences Pub Date : 2023-01-01 DOI: 10.1016/j.ailsci.2023.100078
Negin Sadat Babaiha , Hassan Elsayed , Bide Zhang , Abish Kaladharan , Priya Sethumadhavan , Bruce Schultz , Jürgen Klein , Bruno Freudensprung , Vanessa Lage-Rupprecht , Alpha Tom Kodamullil , Marc Jacobs , Stefan Geissler , Sumit Madan , Martin Hofmann-Apitius
{"title":"A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs","authors":"Negin Sadat Babaiha ,&nbsp;Hassan Elsayed ,&nbsp;Bide Zhang ,&nbsp;Abish Kaladharan ,&nbsp;Priya Sethumadhavan ,&nbsp;Bruce Schultz ,&nbsp;Jürgen Klein ,&nbsp;Bruno Freudensprung ,&nbsp;Vanessa Lage-Rupprecht ,&nbsp;Alpha Tom Kodamullil ,&nbsp;Marc Jacobs ,&nbsp;Stefan Geissler ,&nbsp;Sumit Madan ,&nbsp;Martin Hofmann-Apitius","doi":"10.1016/j.ailsci.2023.100078","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100078","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Corrigendum to “Optimizing active learning for free energy Calculations” [Artificial Intelligence in the Life Sciences, 2 (2022) 100050] “优化自由能计算的主动学习”的勘误表[生命科学中的人工智能，2 (2022)100050]
Artificial intelligence in the life sciences Pub Date : 2023-01-01 DOI: 10.1016/j.ailsci.2023.100074
James Thompson , W Patrick Walters , Jianwen A Feng , Nicolas A Pabon , Hongcheng Xu , Brian B Goldman , Demetri Moustakas , Molly Schmidt , Forrest York
{"title":"Corrigendum to “Optimizing active learning for free energy Calculations” [Artificial Intelligence in the Life Sciences, 2 (2022) 100050]","authors":"James Thompson ,&nbsp;W Patrick Walters ,&nbsp;Jianwen A Feng ,&nbsp;Nicolas A Pabon ,&nbsp;Hongcheng Xu ,&nbsp;Brian B Goldman ,&nbsp;Demetri Moustakas ,&nbsp;Molly Schmidt ,&nbsp;Forrest York","doi":"10.1016/j.ailsci.2023.100074","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100074","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49775001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Erratum regarding missing Conflict of Interest Statement & Ethical Statement in previously published articles 关于先前发表的文章中缺少利益冲突声明和道德声明的勘误表
Artificial intelligence in the life sciences Pub Date : 2023-01-01 DOI: 10.1016/j.ailsci.2023.100076
{"title":"Erratum regarding missing Conflict of Interest Statement & Ethical Statement in previously published articles","authors":"","doi":"10.1016/j.ailsci.2023.100076","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100076","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49775002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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