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

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

Optimizing active learning for free energy calculations 优化自由能计算的主动学习

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100050

James Thompson , W Patrick Walters , Jianwen A Feng , Nicolas A Pabon , Hongcheng Xu , Michael Maser , Brian B Goldman , Demetri Moustakas , Molly Schmidt , Forrest York

{"title":"Optimizing active learning for free energy calculations","authors":"James Thompson , W Patrick Walters , Jianwen A Feng , Nicolas A Pabon , Hongcheng Xu , Michael Maser , Brian B Goldman , Demetri Moustakas , Molly Schmidt , Forrest York","doi":"10.1016/j.ailsci.2022.100050","DOIUrl":"10.1016/j.ailsci.2022.100050","url":null,"abstract":"<div><p>While Relative Binding Free Energy (RBFE) calculations have become a mainstay in lead optimization programs, the computational expense of performing these calculations has limited their broader application. Active learning (AL), a machine learning method used to direct a search iteratively, has explored larger chemical libraries using RBFE calculations. While AL has been successfully applied, there has not been a systematic study of the impact of parameter settings on the performance of AL. To address this gap, we have generated an exhaustive dataset of RBFE calculations on 10,000 congeneric molecules. We used this dataset to explore the impact of several AL design choices, including the number of molecules sampled at each iteration, the method used to select an initial sample, the method used to build a machine learning model, and the acquisition function that defines the balance between exploration and exploitation in the search. Our studies demonstrated that the performance of AL is largely insensitive to the specific machine learning method and acquisition functions used. In our studies, the most significant factor impacting performance was the number of molecules sampled at each iteration where selecting too few molecules hurts performance. Under the best conditions, we were able to identify 75% of the 100 top scoring molecules by sampling only 6% of the dataset. We hope that the dataset of 10K molecules will provide the basis for future studies exploring additional AL strategies. The source code and supporting data for the work are available at <span>https://github.com/google-research/google-research/tree/master/al_for_fep</span><svg><path></path></svg>.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000204/pdfft?md5=fd95fcb1f3da91cd7543db829403ca90&pid=1-s2.0-S2667318522000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48384591","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: 10

Modeling bioconcentration factors in fish with explainable deep learning 利用可解释的深度学习建模鱼类的生物富集因子

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100047

Linlin Zhao , Floriane Montanari , Henry Heberle , Sebastian Schmidt

{"title":"Modeling bioconcentration factors in fish with explainable deep learning","authors":"Linlin Zhao , Floriane Montanari , Henry Heberle , Sebastian Schmidt","doi":"10.1016/j.ailsci.2022.100047","DOIUrl":"10.1016/j.ailsci.2022.100047","url":null,"abstract":"<div><p>The Bioconcentration Factor (BCF) is an important parameter in the environmental risk assessment of chemicals, relevant for industrial and academic research as well as required in many regulatory contexts. It represents the potential of a substance to accumulate in organic tissues or whole animals and is most frequently measured in fish. However, animal welfare reasons, throughput limitations, and costs push the need for alternative methods that allow accurate and reliable estimations of BCF in silico. We present a new deep learning model to predict BCF values from chemical structures, that outperforms currently available models (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.68 and RMSE of 0.59 log units on an external test set; <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.70 and RMSE of 0.74 log units in a demanding cluster split validation). The model is based on molecular representations encoded as CDDD descriptors and exploits a large in-house dataset with measured logD values as an auxiliary task.</p><p>Additionally, we developed a post-hoc explainability method based on SMILES character substitutions to accompany our predictions with atom-level interpretations. These sensitivity scores highlight the most influential moieties in the molecule and can help to understand the predictions better and design new molecules.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000174/pdfft?md5=d1e08bc12ac334ce4c4ea0eb17936560&pid=1-s2.0-S2667318522000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45371673","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

Symbolic regression for the interpretation of quantitative structure-property relationships 符号回归在定量构效关系解释中的应用

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100046

Katsushi Takaki , Tomoyuki Miyao

{"title":"Symbolic regression for the interpretation of quantitative structure-property relationships","authors":"Katsushi Takaki , Tomoyuki Miyao","doi":"10.1016/j.ailsci.2022.100046","DOIUrl":"10.1016/j.ailsci.2022.100046","url":null,"abstract":"<div><p>The interpretation of quantitative structure–activity or structure–property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high predictive abilities. Symbolic regression (SR) combined with genetic programming (GP) is a well-established technique for generating the mathematical expressions that describe the relationships within a dataset. However, SR sometimes produces complicated expressions that are hard for humans to interpret. This paper proposes a method for generating simpler expressions by incorporating three filters into GP-based SR. The filters are further combined with nonlinear least-squares optimization to give filter-introduced GP (FIGP), which improves the predictive ability of SR models while retaining simple expressions. As a proof-of-concept, the quantitative estimate of drug-likeness and the synthetic accessibility score are predicted based on the chemical structures of compounds. Overall, FIGP generates less-complicated expressions than previous SR methods. In terms of predictive ability, FIGP is better than GP, but is outperformed by a support vector machine with a radial basis function kernel. Furthermore, quantitative structure–activity relationship models are constructed for three matching molecular series with biological targets. In the case of one target, the activity prediction models given by FIGP exhibit better predictive ability than multivariate linear regression and support vector regression with the radial basis function kernel, whereas for the remaining cases, FIGP is slightly less accurate than multivariate linear regression.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000162/pdfft?md5=d40d5f4fb6a5861ba6faf6c4bcb2c52c&pid=1-s2.0-S2667318522000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42959550","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

Deepitope: Prediction of HLA-independent T-cell epitopes mediated by MHC class II using a convolutional neural network Deepitope:利用卷积神经网络预测MHC II类介导的HLA非依赖性T细胞表位

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100038

Raphael Trevizani , Fábio Lima Custódio

{"title":"Deepitope: Prediction of HLA-independent T-cell epitopes mediated by MHC class II using a convolutional neural network","authors":"Raphael Trevizani , Fábio Lima Custódio","doi":"10.1016/j.ailsci.2022.100038","DOIUrl":"10.1016/j.ailsci.2022.100038","url":null,"abstract":"<div><p>Computational linear T-cell epitope prediction tools allow cost and labor reduction in downstream <em>in vitro</em> testing, but the quality of currently available methods is compromised by the scarcity of experimental data and extensive HLA polymorphism. However, it is possible to improve prediction quality by forgoing HLA-dependency that allows treating all immunogenic sequences as a single group. This reduces the problem to a much simpler two-classes classification of determining whether a peptide is immunogenic or not. Here, we use a deep convolutional neural network capable of predicting linear T-cell epitope regions in primary structures trained using all peptides deposited in the IEDB website. We also investigate the possibility of using peptides derived from known human proteins as non-immunogenic counterexamples. We compared our model with a state-of-the-art tool and analyze the benefits of using larger databases. Our results corroborate the usefulness of HLA-free methods for practical applications that require the identification of immunogenic sequences. Deepitope is an open source project that can be found at <span>https://github.com/raphaeltrevizani/deepitope</span><svg><path></path></svg>.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"2 ","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318522000095/pdfft?md5=14ba0e71b89c009c171d8f8bde7e5f43&pid=1-s2.0-S2667318522000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43701924","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

Corrigendum to “Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases”[Artificial Intelligence in Life Sciences] 1(2021), 100020 “基于机器学习的COVID-19死亡率预测建议重新定位治疗重症的抗癌药物”[生命科学人工智能]1(2021)，100020

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100032

Thomas Linden , Frank Hanses , Daniel Domingo-Fernández , Lauren Nicole DeLong , Alpha Tom Kodamullil , Jochen Schneider , Maria J.G.T. Vehreschild , Julia Lanznaster , Maria Madeleine Ruethrich , Stefan Borgmann , Martin Hower , Kai Wille , Torsten Feldt , Siegbert Rieg , Bernd Hertenstein , Christoph Wyen , Christoph Roemmele , Jörg Janne Vehreschild , Carolin E.M. Jakob , Melanie Stecher , Holger Fröhlich

引用次数: 0

AI for drug design: From explicit rules to deep learning 药物设计的人工智能:从明确的规则到深度学习

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100041

Lewis Mervin , Samuel Genheden , Ola Engkvist

引用次数: 2

Deep learning of protein–ligand interactions—Remembering the actors 蛋白质-配体相互作用的深度学习——记住参与者

Artificial intelligence in the life sciences Pub Date : 2022-12-01 DOI: 10.1016/j.ailsci.2022.100037

Jürgen Bajorath

引用次数: 2