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  1. AU="Barzilay, Regina"
  2. AU="Schmidt, Michael Rahbek"
  3. AU=Tack J
  4. AU="Oh, Hye Min"
  5. AU=Gaffen Sarah L AU=Gaffen Sarah L
  6. AU="Schmitt, Christine"
  7. AU="McKay, Jackie"
  8. AU="Bellissimo, Catherine A"
  9. AU="Desai, Urja"
  10. AU="Chini, Maria Giovanna"
  11. AU="Xiao, Difei"
  12. AU="Ryan, Chris"
  13. AU="Omar Bazighifan"
  14. AU="Corominas Galbany, Jordi"
  15. AU=Fox Norma E
  16. AU="Hamilton, Shelia M"
  17. AU="Nichols, J Wylie"
  18. AU="Pesce R."
  19. AU="Gambitta, P"
  20. AU="Imran, Aqeel"
  21. AU="Sharma, Yashoda"
  22. AU="Kosai, Jordyn"
  23. AU="Aroca Ferri, María"
  24. AU="Laba, Stephanie"
  25. AU="Kim, Ye-Sel"

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  1. Artikel ; Online: Critical assessment of AI in drug discovery.

    Walters, W Patrick / Barzilay, Regina

    Expert opinion on drug discovery

    2021  Band 16, Heft 9, Seite(n) 937–947

    Abstract: ... ...

    Abstract Introduction
    Mesh-Begriff(e) Artificial Intelligence ; Drug Discovery ; Humans ; Machine Learning
    Sprache Englisch
    Erscheinungsdatum 2021-04-19
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2259618-5
    ISSN 1746-045X ; 1746-0441
    ISSN (online) 1746-045X
    ISSN 1746-0441
    DOI 10.1080/17460441.2021.1915982
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

    Corso, Gabriele / Deng, Arthur / Fry, Benjamin / Polizzi, Nicholas / Barzilay, Regina / Jaakkola, Tommi

    ArXiv

    2024  

    Abstract: Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. ... ...

    Abstract Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks. Further, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods.
    Sprache Englisch
    Erscheinungsdatum 2024-02-28
    Erscheinungsland United States
    Dokumenttyp Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Learning to Split for Automatic Bias Detection

    Bao, Yujia / Barzilay, Regina

    2022  

    Abstract: Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label pairs, ls learns to split this dataset so that predictors trained on the ... ...

    Abstract Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label pairs, ls learns to split this dataset so that predictors trained on the training split cannot generalize to the testing split. This performance gap suggests that the testing split is under-represented in the dataset, which is a signal of potential bias. Identifying non-generalizable splits is challenging since we have no annotations about the bias. In this work, we show that the prediction correctness of each example in the testing split can be used as a source of weak supervision: generalization performance will drop if we move examples that are predicted correctly away from the testing split, leaving only those that are mis-predicted. ls is task-agnostic and can be applied to any supervised learning problem, ranging from natural language understanding and image classification to molecular property prediction. Empirical results show that ls is able to generate astonishingly challenging splits that correlate with human-identified biases. Moreover, we demonstrate that combining robust learning algorithms (such as group DRO) with splits identified by ls enables automatic de-biasing. Compared to previous state-of-the-art, we substantially improve the worst-group performance (23.4% on average) when the source of biases is unknown during training and validation.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-04-28
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: Applications of Deep Learning in Molecule Generation and Molecular Property Prediction.

    Walters, W Patrick / Barzilay, Regina

    Accounts of chemical research

    2020  Band 54, Heft 2, Seite(n) 263–270

    Abstract: Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, ... ...

    Abstract Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure-activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.
    Sprache Englisch
    Erscheinungsdatum 2020-12-28
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 1483291-4
    ISSN 1520-4898 ; 0001-4842
    ISSN (online) 1520-4898
    ISSN 0001-4842
    DOI 10.1021/acs.accounts.0c00699
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  5. Buch ; Online: Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design

    Stärk, Hannes / Jing, Bowen / Barzilay, Regina / Jaakkola, Tommi

    2023  

    Abstract: A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards ... ...

    Abstract A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards this goal, we first develop HarmonicFlow, an improved generative process over 3D protein-ligand binding structures based on our self-conditioned flow matching objective. FlowSite extends this flow model to jointly generate a protein pocket's discrete residue types and the molecule's binding 3D structure. We show that HarmonicFlow improves upon state-of-the-art generative processes for docking in simplicity, generality, and average sample quality in pocket-level docking. Enabled by this structure modeling, FlowSite designs binding sites substantially better than baseline approaches.

    Comment: Under review. 25 pages, 12 figures
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-10-09
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Reply to M. Eriksson et al and Z. Jin et al.

    Yala, Adam / Mikhael, Peter G / Hughes, Kevin / Barzilay, Regina

    Journal of clinical oncology : official journal of the American Society of Clinical Oncology

    2022  Band 40, Heft 20, Seite(n) 2281–2282

    Sprache Englisch
    Erscheinungsdatum 2022-04-22
    Erscheinungsland United States
    Dokumenttyp Letter ; Comment
    ZDB-ID 604914-x
    ISSN 1527-7755 ; 0732-183X
    ISSN (online) 1527-7755
    ISSN 0732-183X
    DOI 10.1200/JCO.22.00292
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  7. Artikel ; Online: MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation.

    Qian, Yujie / Guo, Jiang / Tu, Zhengkai / Li, Zhening / Coley, Connor W / Barzilay, Regina

    Journal of chemical information and modeling

    2023  Band 63, Heft 7, Seite(n) 1925–1934

    Abstract: Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In ... ...

    Abstract Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and bonds, along with their geometric layouts, to construct the molecular structure. Our model flexibly incorporates symbolic chemistry constraints to recognize chirality and expand abbreviated structures. We further develop data augmentation strategies to enhance the model robustness against domain shifts. In experiments on both synthetic and realistic molecular images, MolScribe significantly outperforms previous models, achieving 76-93% accuracy on public benchmarks. Chemists can also easily verify MolScribe's prediction, informed by its confidence estimation and atom-level alignment with the input image. MolScribe is publicly available through Python and web interfaces: https://github.com/thomas0809/MolScribe.
    Mesh-Begriff(e) Molecular Structure ; Benchmarking
    Sprache Englisch
    Erscheinungsdatum 2023-03-27
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.2c01480
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  8. Artikel ; Online: RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing.

    Qian, Yujie / Guo, Jiang / Tu, Zhengkai / Coley, Connor W / Barzilay, Regina

    Journal of chemical information and modeling

    2023  Band 63, Heft 13, Seite(n) 4030–4041

    Abstract: Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we ... ...

    Abstract Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe.
    Mesh-Begriff(e) Machine Learning
    Sprache Englisch
    Erscheinungsdatum 2023-06-27
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c00439
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  9. Artikel ; Online: Artificial Intelligence and Machine Learning in Lung Cancer Screening.

    Adams, Scott J / Mikhael, Peter / Wohlwend, Jeremy / Barzilay, Regina / Sequist, Lecia V / Fintelmann, Florian J

    Thoracic surgery clinics

    2023  Band 33, Heft 4, Seite(n) 401–409

    Abstract: Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML ... ...

    Abstract Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.
    Mesh-Begriff(e) Humans ; Early Detection of Cancer ; Artificial Intelligence ; Lung Neoplasms/diagnostic imaging ; Machine Learning ; Tomography, X-Ray Computed
    Sprache Englisch
    Erscheinungsdatum 2023-05-12
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2149218-9
    ISSN 1558-5069 ; 1547-4127
    ISSN (online) 1558-5069
    ISSN 1547-4127
    DOI 10.1016/j.thorsurg.2023.03.001
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  10. Buch ; Online: Fast non-autoregressive inverse folding with discrete diffusion

    Yang, John J. / Yim, Jason / Barzilay, Regina / Jaakkola, Tommi

    2023  

    Abstract: Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this eschews higher order interactions that could be exploited to improve ... ...

    Abstract Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this eschews higher order interactions that could be exploited to improve inference speed. We describe a non-autoregressive alternative that performs inference using a constant number of calls resulting in a 23 times speed up without a loss in performance on the CATH benchmark. Conditioned on the 3D structure, we fine-tune ProteinMPNN to perform discrete diffusion with a purity prior over the index sampling order. Our approach gives the flexibility in trading off inference speed and accuracy by modulating the diffusion speed. Code: https://github.com/johnyang101/pmpnndiff

    Comment: NeurIPS Machine learning for Stuctural Biology workshop
    Schlagwörter Quantitative Biology - Biomolecules ; Statistics - Machine Learning
    Erscheinungsdatum 2023-12-04
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