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Context-dependent similarity analysis of analogue series for structure-activity relationship transfer based on a concept from natural language processing

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JOURNAL OF CHEMINFORMATICS 2025年第1期17卷 1-14页

作者： Yoshimori, Atsushi Bajorath, Jurgen Inst Theoret Med Inc 26-1 Muraoka Higashi 2 chome Fujisawa Kanagawa 2510012 Japan Univ Bonn Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artif Intelligence Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Analogue series (AS) are generated during compound optimization in medicinal chemistry and are the major source of structure-activity relationship (SAR) information. Pairs of active AS consisting of compounds with corresponding substituents and comparable potency progression represent SAR transfer events for the same target or across different targets. We report a new computational approach to systematically search for SAR transfer series that combines an AS alignment algorithm with context-depending similarity assessment based on vector embeddings adapted from natural language processing. The methodology comprehensively accounts for substituent similarity, identifies non-classical bioisosteres, captures substituent-property relationships, and generates accurate AS alignments. Context-dependent similarity assessment is conceptually novel in computational medicinal chemistry and should also be of interest for other *** contributionA method is reported to systematically search for and align analogue series with SAR transfer potential. Central to the approach is the assessment of context-dependent similarity for substituents, a new concept in cheminformatics, which is based upon vector embeddings and word pair relationships adapted from natural language processing.

关键词： Active analogue series Potency progression Series alignment SAR transfer Fragment pair relationships Context-dependent similarity

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Self-Organizing Transformations for Automatic Feature Engineering

Self-Organizing Transformations for Automatic Feature Engine...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Rodrigues, Ericks da Silva Lima Martins, Denis Mayr de Lima Neto, Fernando Buarque Univ Pernambuco Comp Engn Program Recife PE Brazil Univ Munster ERCIS Machine Learning & Data Engn Muesnter Germany

ISBN: (纸本)9781728190488

Feature Engineering (FE) consists of generating new, better features to improve machine learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose the Self-Organizing Automatic Feature Engineering (SOAFE), a novel approach for Automatic Feature Engineering (AFE). Different from the majority of the existing AFEs, SOAFE employs an unsupervised technique (Self-Organizing Maps) to identify patterns in the data, and apply a form of cooperative training, inspired by Generative Adversarial Networks, to improve the feature construction. Our results on several datasets show that SOAFE can improve classification models when compared with existing AFE approaches.

关键词： feature engineering automatic feature engineering self-organizing maps machine learning

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MapView: Exploring Datasets via Unsupervised View Recommendation

MapView: Exploring Datasets via Unsupervised View Recommenda...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Aires de Carvalho, Thiego Buenos Lima Martins, Denis Mayr de Lima Neto, Fernando Buarque Univ Pernambuco Comp Engn Program Recife PE Brazil Univ Munster ERCIS Machine Learning & Data Engn Muesnter Germany

ISBN: (纸本)9781728188645

Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user's intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.

关键词： view recommendation data exploration self-organizing maps

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Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues

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BME FRONTIERS 2025年 6卷 0110页

作者： Fang, Zhou Krusen, Kelsey Priest, Hannah Wang, Mingshuang Kim, Sungwoong Sriram, Anirudh Yellanki, Ashritha Singh, Ankur Horwitz, Edwin Coskun, Ahmet F. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA Georgia Inst Technol Machine Learning Grad Program Atlanta GA USA Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA USA Georgia Inst Technol Interdisciplinary Bioengn Grad Program Atlanta GA 30332 USA Parker H Petit Inst Bioengn & Biosci Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Sch Med Dept Pediat Atlanta GA 30322 USA

Objective: We developed 3-dimensional spatially resolved gene neighborhood network embedding (3D-spaGNN-E) to find subcellular gene proximity relationships and identify key subcellular motifs in cell-cell communication (CCC). Impact Statement: The pipeline combines 3D imaging-based spatial transcriptomics and graph-based deep learning to identify subcellular motifs. Introduction: Advancements in imaging and experimental technology allow the study of 3D spatially resolved transcriptomics and capture better spatial context than approximating the samples as 2D. However, the third spatial dimension increases the data complexity and requires new analyses. Methods: 3D-spaGNN-E detects single transcripts in 3D cell culture samples and identifies subcellular gene proximity relationships. Then, a graph autoencoder projects the gene proximity relationships into a latent space. We then applied explainability analysis to identify subcellular CCC motifs. Results: We first applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogel. After clustering the cells based on the RNA count, we identified cells belonging to the same cluster as homotypic and those belonging to different clusters as heterotypic. We identified changes in local gene proximity near the border between homotypic and heterotypic cells. When applying the pipeline to the MSC-peripheral blood mononuclear cell (PBMC) coculture system, we identified CD4+ and CD8+ T cells. Local gene proximity and autoencoder embedding changes can distinguish strong and weak suppression of different immune cells. Lastly, we compared astrocyte-neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence in situ hybridization (MERFISH) data and identified regional gene proximity differences. Conclusion: 3D-spaGNN-E distinguished distinct CCCs in cell culture and tissue by examining subcellular motifs.

关键词： Stem cells

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Detecting suicide risk among US servicemembers and veterans: a deep learning approach using social media data

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PSYCHOLOGICAL MEDICINE 2024年第12期54卷 3379-3388页

作者： Zuromski, Kelly L. Low, Daniel M. Jones, Noah C. Kuzma, Richard Kessler, Daniel Zhou, Liutong Kastman, Erik K. Epstein, Jonathan Madden, Carlos Ghosh, Satrajit S. Gowel, David Nock, Matthew K. Harvard Univ Dept Psychol Cambridge MA 02138 USA Franciscan Childrens Brighton MA 02135 USA Harvard Med Sch Speech & Hearing Biosci & Technol Program Boston MA USA MIT McGovern Inst Brain Res Cambridge MA USA MIT MIT Media Lab Cambridge MA USA Amazon Web Serv Machine Learning Solut Lab New York NY USA RallyPoint Networks Inc Boston MA USA

Background Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media *** Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such *** The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular *** Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

关键词： machine learning military natural language processing suicide veterans

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Extension of multi-site analogue series with potent compounds using a bidirectional transformer-based chemical language model

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RSC MEDICINAL CHEMISTRY 2024年第7期15卷 2527-2537页

作者： Chen, Hengwei Yoshimori, Atsushi Bajorath, Juergen Univ Bonn Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Inst Theoret Med Inc 26-1 Muraoka Higashi 2 Chome Fujisawa Kanagawa 2510012 Japan Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Generating potent compounds for evolving analogue series (AS) is a key challenge in medicinal chemistry. The versatility of chemical language models (CLMs) makes it possible to formulate this challenge as an off-the-beaten-path prediction task. In this work, we have devised a coding and tokenization scheme for evolving AS with multiple substitution sites (multi-site AS) and implemented a bidirectional transformer to predict new potent analogues for such series. Scientific foundations of this approach are discussed and, as a benchmark, the transformer model is compared to a recurrent neural network (RNN) for the prediction of analogues of AS with single substitution sites. Furthermore, the transformer is shown to successfully predict potent analogues with varying R-group combinations for multi-site AS having activity against many different targets. Prediction of R-group combinations for extending AS with potent compounds represents a novel approach for compound optimization. Shown is the extension of an analogue series with a new potent compound using a chemical language model. Substitution sites and non-hydrogen R-groups are colored in red (the log-likelihood score for the new analogue is reported in parentheses).

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MolAnchor method for explaining compound predictions based on substructures

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY REPORTS 2024年 12卷

作者： Lamens, Alec Bajorath, Juergen Rheinische Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit B ITChem Biol & Med Chem Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

In medicinal chemistry, the impact of machine learning remains limited if predictions are not understood, which often precludes experimental follow-up. Therefore, chemically intuitive approaches that aid in model understanding and interpretation at the molecular level of detail are sought after. While feature attribution methods quantifying feature importance for model decisions are widely used in many areas, they must typically be combined with visualization techniques, if possible, to render the results accessible from a chemical viewpoint. On the other hand, there are approaches such as counterfactuals that yield closely related chemical structures with different prediction outcomes, providing direct access to structural features that critically influence model decisions. Herein, we introduce another approach designed to rationalize chemical predictions based on molecular structure. Therefore, we adapt principles underlying the anchor concept from explainable artificial intelligence (XAI) and alter them for molecular machine learning. The resulting method, termed MolAnchor, systematically identifies substructures in test compounds that determine property predictions, thus ensuring chemical interpretability. The MolAnchor methodology is made freely available to the medicinal chemistry community as a part of our study.

关键词： Molecular machine learning Model interpretation Explainable artificial intelligence Anchors Molecular fragments

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Genome-wide CRISPR guide RNA design and specificity analysis with GuideScan2

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GENOME BIOLOGY 2025年第1期26卷 1-25页

作者： Schmidt, Henri Zhang, Minsi Chakarov, Dimitar Bansal, Vineet Mourelatos, Haralambos Sanchez-Rivera, Francisco J. Lowe, Scott W. Ventura, Andrea Leslie, Christina S. Pritykin, Yuri Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Mem Sloan Kettering Canc Ctr Computat & Syst Biol Program New York NY 10021 USA Mem Sloan Kettering Canc Ctr Canc Biol & Genet Program New York NY 10021 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ USA Mem Sloan Kettering Tri Inst MD PhD Program Weill Cornell Rockefeller New York NY USA MIT David H Koch Inst Integrat Canc Res Cambridge MA USA MIT Dept Biol Cambridge MA USA

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly design and analysis of individual gRNAs and gRNA libraries for targeting coding and non-coding regions in custom genomes. GuideScan2 analysis identifies widespread confounding effects of low-specificity gRNAs in published CRISPR screens and enables construction of a gRNA library that reduces off-target effects in a gene essentiality screen. GuideScan2 also enables the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome. GuideScan2 will facilitate CRISPR experiments across a wide range of applications.

关键词： CRISPR Guide RNA Off-targets Burrows-Wheeler transform Algorithm Software Web interface GuideScan2

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Detection of Leukemia from Histopathological Image using Deep learning Techniques 2

Detection of Leukemia from Histopathological Image using Dee...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Kumar, V Sunil Renukadevi, S. Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Gupta, Deepak Khanna, Ashish Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India

ISBN: (纸本)9781665493963

Leukemia is a kind of cancer that is brought on by abnormalities in the body's white blood cells. An inspection of a blood smear that has been collected from the peripheral blood and then seen under a microscope is the method that is considered to be the gold standard for diagnosing leukemia. Techniques such as counting the cells in the blood and doing morphological analysis are two examples of methods that may be used to identify and diagnose leukemia. It is necessary to carefully examine a stained blood smear or an aspiration of bone marrow in order to correctly detect and diagnose leukemia. Both of these procedures involve the removal of bone marrow. This requires prior training and specialized expertise on the subject matter. A tedious and time-consuming operation, the manual evaluation of microscopic pictures of blood samples is one of the steps in the diagnostic procedure. The results are highly dependent on the degree of knowledge and experience that the laboratory worker has. Pathologists are often burdened with an excessive quantity of such data that has to be properly evaluated in order to arrive at a decision. This may be a time-consuming and difficult process. Red blood cells, white blood cells, and blood platelets are the three primary cell types that make up human blood. Platelets are also present in blood. Erythrocyte disease might refer to a collection of diseases that have an effect on hemoglobin. Hemoglobin is a protein found in red blood cells that is responsible for transporting oxygen to cells located throughout the body. This condition is referred to as sickle cell anemia. In sickle cell anemia, the blood contains abnormal hemoglobin molecules known as hemoglobin S. These molecules distort the structure of red blood cells, causing them to take on the form of a crescent or a reaping hook. There is a genetic form of anemia known as sickle cell anemia. This kind of anemia is caused by defective hemoglobin, which causes red blood cells to take on t

关键词： Hemoglobin

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Decoding senescence of aging single cells at the nexus of biomaterials, microfluidics, and spatial omics

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NPJ AGING 2024年第1期10卷 57页

作者： Venkataraman, Abhijeet Kordic, Ivan Li, Jiaxun Zhang, Nicholas Bharadwaj, Nivik Sanjay Fang, Zhou Das, Sandip Coskun, Ahmet F. Emory Univ Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Parker H Petit Inst Bioengn & Biosci 315 Ferst Dr NW Atlanta GA 30332 USA Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA USA Georgia Inst Technol Interdisciplinary Bioengn Grad Program Atlanta GA 30332 USA Georgia Inst Technol Machine Learning Grad Program Atlanta GA USA

Aging has profound effects on the body, most notably an increase in the prevalence of several diseases. An important aging hallmark is the presence of senescent cells that no longer multiply nor die off properly. Another characteristic is an altered immune system that fails to properly self-surveil. In this multi-player aging process, cellular senescence induces a change in the secretory phenotype, known as senescence-associated secretory phenotype (SASP), of many cells with the intention of recruiting immune cells to accelerate the clearance of these damaged senescent cells. However, the SASP phenotype results in inducing secondary senescence of nearby cells, resulting in those cells becoming senescent, and improper immune activation resulting in a state of chronic inflammation, called inflammaging, in many diseases. Senescence in immune cells, termed immunosenescence, results in further dysregulation of the immune system. An interdisciplinary approach is needed to physiologically assess aging changes of the immune system at the cellular and tissue level. Thus, the intersection of biomaterials, microfluidics, and spatial omics has great potential to collectively model aging and immunosenescence. Each of these approaches mimics unique aspects of the body undergoes as a part of aging. This perspective highlights the key aspects of how biomaterials provide non-cellular cues to cell aging, microfluidics recapitulate flow-induced and multi-cellular dynamics, and spatial omics analyses dissect the coordination of several biomarkers of senescence as a function of cell interactions in distinct tissue environments. An overview of how senescence and immune dysregulation play a role in organ aging, cancer, wound healing, Alzheimer's, and osteoporosis is included. To illuminate the societal impact of aging, an increasing trend in anti-senescence and anti-aging interventions, including pharmacological interventions, medical procedures, and lifestyle changes is discussed, includ

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