Artificial intelligence in the life sciences最新文献_第7页

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 : 2023-12-01 Epub Date: 2023-02-04 DOI: 10.1016/j.ailsci.2023.100064

Martin Vogt

引用次数: 2

Combining molecular and cell painting image data for mechanism of action prediction 结合分子和细胞绘画图像数据进行作用机理预测

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-02-17 DOI: 10.1016/j.ailsci.2023.100060

Guangyan Tian , Philip J Harrison , Akshai P Sreenivasan , Jordi Carreras-Puigvert , Ola Spjuth

{"title":"Combining molecular and cell painting image data for mechanism of action prediction","authors":"Guangyan Tian , Philip J Harrison , Akshai P Sreenivasan , Jordi Carreras-Puigvert , Ola Spjuth","doi":"10.1016/j.ailsci.2023.100060","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100060","url":null,"abstract":"<div><p>The mechanism of action (MoA) of a compound describes the biological interaction through which it produces a pharmacological effect. Multiple data sources can be used for the purpose of predicting MoA, including compound structural information, and various assays, such as those based on cell morphology, transcriptomics and metabolomics. In the present study we explored the benefits and potential additive/synergistic effects of combining structural information, in the form of Morgan fingerprints, and morphological information, in the form of five-channel Cell Painting image data. For a set of 10 well represented MoA classes, we compared the performance of deep learning models trained on the two datasets separately versus a model trained on both datasets simultaneously. On a held-out test set we obtained a macro-averaged F1 score of 0.58 when training on only the structural data, 0.81 when training on only the image data, and 0.92 when training on both together. Thus indicating clear additive/synergistic effects and highlighting the benefit of integrating multiple data sources for MoA prediction.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49774973","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

Specific contributions of artificial intelligence to interdisciplinary life science research – exploring and communicating new opportunities 人工智能对跨学科生命科学研究的具体贡献——探索和交流新机遇

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2022-12-11 DOI: 10.1016/j.ailsci.2022.100052

Jürgen Bajorath

引用次数: 0

Designing microplate layouts using artificial intelligence 利用人工智能设计微孔板布局

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-04-14 DOI: 10.1016/j.ailsci.2023.100073

María Andreína Francisco Rodríguez, Jordi Carreras Puigvert, Ola Spjuth

{"title":"Designing microplate layouts using artificial intelligence","authors":"María Andreína Francisco Rodríguez, Jordi Carreras Puigvert, Ola Spjuth","doi":"10.1016/j.ailsci.2023.100073","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100073","url":null,"abstract":"<div><p>Microplates are indispensable in large-scale biomedical experiments but the physical location of samples and controls on the microplate can significantly affect the resulting data and quality metric values. We introduce a new method based on constraint programming for designing microplate layouts that reduces unwanted bias and limits the impact of batch effects after error correction and normalisation. We demonstrate that our method applied to dose-response experiments leads to more accurate regression curves and lower errors when estimating <span><math><msub><mtext>IC</mtext><mn>50</mn></msub></math></span>/<span><math><msub><mtext>EC</mtext><mn>50</mn></msub></math></span>, and for drug screening leads to increased precision, when compared to random layouts. It also reduces the risk of inflated scores from common microplate quality assessment metrics such as <span><math><msup><mi>Z</mi><mo>′</mo></msup></math></span> factor and SSMD. We make our method available via a suite of tools (PLAID) including a reference constraint model, a web application, and Python notebooks to evaluate and compare designs when planning microplate experiments.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49774976","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 : 2023-12-01 Epub Date: 2023-05-06 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

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 : 2023-12-01 Epub Date: 2023-05-11 DOI: 10.1016/j.ailsci.2023.100076

引用次数: 0

Trends and challenges in chemoinformatics research in Latin America 拉丁美洲化学信息学研究的趋势和挑战

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-05-11 DOI: 10.1016/j.ailsci.2023.100077

Jazmín Miranda-Salas , Carlos Peña-Varas , Ignacio Valenzuela Martínez , Dionisio A. Olmedo , William J. Zamora , Miguel Angel Chávez-Fumagalli , Daniela Q. Azevedo , Rachel Oliveira Castilho , Vinicius G. Maltarollo , David Ramírez , José L. Medina-Franco

{"title":"Trends and challenges in chemoinformatics research in Latin America","authors":"Jazmín Miranda-Salas , Carlos Peña-Varas , Ignacio Valenzuela Martínez , Dionisio A. Olmedo , William J. Zamora , Miguel Angel Chávez-Fumagalli , Daniela Q. Azevedo , Rachel Oliveira Castilho , Vinicius G. Maltarollo , David Ramírez , José L. Medina-Franco","doi":"10.1016/j.ailsci.2023.100077","DOIUrl":"10.1016/j.ailsci.2023.100077","url":null,"abstract":"<div><p>Chemoinformatics is an independent inter-discipline with a broad impact in drug design and discovery, medicinal chemistry, biochemistry, analytical and organic chemistry, natural products, and several other areas in chemistry. Through collaborations, scientific exchanges, and participation in international research networks, Latin American scientists have contributed to the development of this subject. The aim of this perspective is to discuss the status and progress of the chemoinformatic discipline in Latin America. We team up to provide an author´s perspective on the topics that have been investigated and published over the past twelve years, collaborations between Latin America researchers and others worldwide, contributions to open-access chemoinformatic tools such as web servers, and educational-related resources and events, such as scientific conferences. We conclude that linking and fostering collaboration within each nation as well as among other Latin American nations and globally is made possible by open science and the democratization of science. We also outline strategic actions that can boost the development and practice of chemoinformatic in the region and enhance the interaction between Latin American countries and the rest of the world.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42646088","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

Data science and data analytics in life science research 生命科学研究中的数据科学和数据分析

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-02-27 DOI: 10.1016/j.ailsci.2023.100067

Jürgen Bajorath

引用次数: 1

Bayesian optimization for ternary complex prediction (BOTCP) 基于贝叶斯优化的三元复变预测

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-04-19 DOI: 10.1016/j.ailsci.2023.100072

Arjun Rao , Tin M. Tunjic , Michael Brunsteiner , Michael Müller, Hosein Fooladi, Chiara Gasbarri, Noah Weber

{"title":"Bayesian optimization for ternary complex prediction (BOTCP)","authors":"Arjun Rao , Tin M. Tunjic , Michael Brunsteiner , Michael Müller, Hosein Fooladi, Chiara Gasbarri, Noah Weber","doi":"10.1016/j.ailsci.2023.100072","DOIUrl":"https://doi.org/10.1016/j.ailsci.2023.100072","url":null,"abstract":"<div><p>Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.</p><p>We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. We show how a fitness score combining an estimation of protein-protein interactions with PROTAC conformation energy calculations enables the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. We evaluate our method using DockQ scores on a number of available ternary complex structures (including previously unevaluated cases) and demonstrate that even with a clustering that requires members to have a high similarity, i.e., with smaller clusters, we can assign high ranks to those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. We also demonstrate the resultant improved yield of near-native poses<span><sup>3</sup></span> in these clusters.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49775003","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

Deep metric learning for the classification of MALDI-TOF spectral signatures from multiple species of neotropical disease vectors 多种新热带病媒MALDI-TOF谱特征分类的深度度量学习

Artificial intelligence in the life sciences Pub Date : 2023-12-01 Epub Date: 2023-04-06 DOI: 10.1016/j.ailsci.2023.100071

Fernando Merchan , Kenji Contreras , Rolando A. Gittens , Jose R. Loaiza , Javier E. Sanchez-Galan

{"title":"Deep metric learning for the classification of MALDI-TOF spectral signatures from multiple species of neotropical disease vectors","authors":"Fernando Merchan , Kenji Contreras , Rolando A. Gittens , Jose R. Loaiza , Javier E. Sanchez-Galan","doi":"10.1016/j.ailsci.2023.100071","DOIUrl":"10.1016/j.ailsci.2023.100071","url":null,"abstract":"<div><p>Deep Learning techniques have significant advantages for mass spectral classification, such as parallelized signal correction and feature extraction. Deep Metric Learning models combine Metric Learning to determine the degree of similarity or difference between a set of mass spectra with the generalization power of Deep Learning to improve feature extraction even further. The two most popular of these models combine multiple neural networks with identical architectures and are commonly called Siamese (SNN) and Triplet Neural Networks (TNN). Herein, using both SNNs and TNNs, we intended to taxonomically categorize two sets of previously-validated mass spectra that corresponded to 30 species of Neotropical arthropods in the Culicidae and Ixodidae families, some of which are disease vectors. The effectiveness of SNNs and TNNs to correctly classify 826 spectra from 12 mosquito species and 310 spectra from 18 species of hard ticks was highly effective, with both algorithms performing with minimal average loss during cross-validation. SNNs produced accuracy rates for ticks and mosquitoes of 91.22% and 94.46%, respectively, while accuracy rates of 93% and 99% were obtained with TNNs. Our results indicate that Deep Metric Learning is a practical machine learning tool for quickly and precisely classifying MALDI-TOF-generated mass spectra of Neotropical and public-health-relevant arthropod species.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100071"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41748999","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