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  1. Article ; Online: PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data.

    Torrens-Fontanals, Mariona / Tourlas, Panagiotis / Doerr, Stefan / De Fabritiis, Gianni

    Journal of chemical information and modeling

    2024  Volume 64, Issue 3, Page(s) 584–589

    Abstract: PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts. By harnessing state-of-the-art web technologies such as ... ...

    Abstract PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts. By harnessing state-of-the-art web technologies such as WebAssembly, PlayMolecule Viewer integrates powerful Python libraries directly within the browser environment, which enhances its capabilities to manage multiple types of molecular data. With its intuitive interface, it allows users to easily upload, visualize, select, and manipulate molecular structures and associated data. The toolkit supports a wide range of common structural file formats and offers a variety of molecular representations to cater to different visualization needs. PlayMolecule Viewer is freely accessible at open.playmolecule.org, ensuring accessibility and availability to the scientific community and beyond.
    MeSH term(s) Software ; Molecular Structure ; Computational Biology
    Language English
    Publishing date 2024-01-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01776
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: TensorNet

    Simeon, Guillem / de Fabritiis, Gianni

    Cartesian Tensor Representations for Efficient Learning of Molecular Potentials

    2023  

    Abstract: The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages ... ...

    Abstract The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. By using Cartesian tensor atomic embeddings, feature mixing is simplified through matrix product operations. Furthermore, the cost-effective decomposition of these tensors into rotation group irreducible representations allows for the separate processing of scalars, vectors, and tensors when necessary. Compared to higher-rank spherical tensor models, TensorNet demonstrates state-of-the-art performance with significantly fewer parameters. For small molecule potential energies, this can be achieved even with a single interaction layer. As a result of all these properties, the model's computational cost is substantially decreased. Moreover, the accurate prediction of vector and tensor molecular quantities on top of potential energies and forces is possible. In summary, TensorNet's framework opens up a new space for the design of state-of-the-art equivariant models.

    Comment: NeurIPS 2023, camera-ready version
    Keywords Computer Science - Machine Learning ; Physics - Chemical Physics ; Physics - Computational Physics
    Subject code 006
    Publishing date 2023-06-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

    Navarro, Carles / Majewski, Maciej / De Fabritiis, Gianni

    Journal of chemical theory and computation

    2023  Volume 19, Issue 21, Page(s) 7518–7526

    Abstract: Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and ... ...

    Abstract Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.
    MeSH term(s) Proteins/chemistry ; Protein Conformation ; Molecular Dynamics Simulation ; Machine Learning ; Protein Folding
    Chemical Substances Proteins
    Language English
    Publishing date 2023-10-24
    Publishing country United States
    Document type Journal Article
    ISSN 1549-9626
    ISSN (online) 1549-9626
    DOI 10.1021/acs.jctc.3c00638
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics.

    Herrera-Nieto, Pablo / Pérez, Adrià / De Fabritiis, Gianni

    Journal of chemical theory and computation

    2023  Volume 19, Issue 13, Page(s) 3817–3824

    Abstract: Intrinsically disordered proteins participate in many biological processes by folding upon binding to other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is ... ...

    Abstract Intrinsically disordered proteins participate in many biological processes by folding upon binding to other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is whether folding occurs prior to or after binding. Here we use a novel, unbiased, high-throughput adaptive sampling approach to reconstruct the binding and folding between the disordered transactivation domain of c-Myb and the KIX domain of the CREB-binding protein. The reconstructed long-term dynamical process highlights the binding of a short stretch of amino acids on c-Myb as a folded α-helix. Leucine residues, especially Leu298-Leu302, establish initial native contacts that prime the binding and folding of the rest of the peptide, with a mixture of conformational selection on the N-terminal region with an induced fit of the C-terminal.
    MeSH term(s) Intrinsically Disordered Proteins/chemistry ; Molecular Dynamics Simulation ; Protein Folding ; Education, Distance ; Protein Binding
    Chemical Substances Intrinsically Disordered Proteins
    Language English
    Publishing date 2023-06-21
    Publishing country United States
    Document type Journal Article
    ISSN 1549-9626
    ISSN (online) 1549-9626
    DOI 10.1021/acs.jctc.3c00008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: TorchMD-NET

    Thölke, Philipp / De Fabritiis, Gianni

    Equivariant Transformers for Neural Network based Molecular Potentials

    2022  

    Abstract: The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining ... ...

    Abstract The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain valuable insights into the black box predictor and show differences in the learned representation of conformers versus conformations sampled from molecular dynamics or normal modes. Furthermore, we highlight the importance of datasets including off-equilibrium conformations for the evaluation of molecular potentials.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Physics - Chemical Physics
    Subject code 006
    Publishing date 2022-02-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics

    Herrera-Nieto, Pablo / Pérez, Adrià / De Fabritiis, Gianni

    2023  

    Abstract: Intrinsically disordered proteins participate in many biological processes by folding upon binding with other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is ... ...

    Abstract Intrinsically disordered proteins participate in many biological processes by folding upon binding with other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is whether folding occurs prior to or after binding. Here we use a novel unbiased high-throughput adaptive sampling approach to reconstruct the binding and folding between the disordered transactivation domain of \mbox{c-Myb} and the KIX domain of the CREB-binding protein. The reconstructed long-term dynamical process highlights the binding of a short stretch of amino acids on \mbox{c-Myb} as a folded $\alpha$-helix. Leucine residues, specially Leu298 to Leu302, establish initial native contacts that prime the binding and folding of the rest of the peptide, with a mixture of conformational selection on the N-terminal region with an induced fit of the C-terminal.
    Keywords Quantitative Biology - Biomolecules ; Computer Science - Machine Learning ; Physics - Computational Physics
    Subject code 612
    Publishing date 2023-02-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Top-down machine learning of coarse-grained protein force-fields

    Navarro, Carles / Majewski, Maciej / de Fabritiis, Gianni

    2023  

    Abstract: Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales. Our methodology involves simulating proteins with molecular dynamics and ... ...

    Abstract Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov State Models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.
    Keywords Quantitative Biology - Biomolecules ; Computer Science - Machine Learning
    Subject code 612
    Publishing date 2023-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials.

    Sabanés Zariquiey, Francesc / Galvelis, Raimondas / Gallicchio, Emilio / Chodera, John D / Markland, Thomas E / De Fabritiis, Gianni

    Journal of chemical information and modeling

    2024  Volume 64, Issue 5, Page(s) 1481–1485

    Abstract: This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute ... ...

    Abstract This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
    MeSH term(s) Ligands ; Thermodynamics ; Proteins/chemistry ; Protein Binding ; Molecular Dynamics Simulation ; Neural Networks, Computer
    Chemical Substances Ligands ; Proteins
    Language English
    Publishing date 2024-02-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c02031
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
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  9. Article ; Online: Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials.

    Zariquiey, Francesc Sabanes / Galvelis, Raimondas / Gallicchio, Emilio / Chodera, John D / Markland, Thomas E / de Fabritiis, Gianni

    ArXiv

    2024  

    Abstract: This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute ... ...

    Abstract This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.
    Language English
    Publishing date 2024-02-14
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

    Sabanés Zariquiey, Francesc / Pérez, Adrià / Majewski, Maciej / Gallicchio, Emilio / De Fabritiis, Gianni

    Journal of chemical information and modeling

    2023  Volume 63, Issue 8, Page(s) 2438–2444

    Abstract: The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the ... ...

    Abstract The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.
    MeSH term(s) Thermodynamics ; Molecular Dynamics Simulation ; Reproducibility of Results ; Entropy ; Protein Binding ; Ligands
    Chemical Substances Ligands
    Language English
    Publishing date 2023-04-12
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; 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.3c00178
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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