NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929639479312384 |
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| author | Qiao, Zhuoran Ding, Feizhi Dresselhaus, Thomas Rosenfeld, Mia A. Han, Xiaotian Howell, Owen Iyengar, Aniketh Opalenski, Stephen Christensen, Anders S. Sirumalla, Sai Krishna Manby, Frederick R. Miller III, Thomas F. Welborn, Matthew |
| author_facet | Qiao, Zhuoran Ding, Feizhi Dresselhaus, Thomas Rosenfeld, Mia A. Han, Xiaotian Howell, Owen Iyengar, Aniketh Opalenski, Stephen Christensen, Anders S. Sirumalla, Sai Krishna Manby, Frederick R. Miller III, Thomas F. Welborn, Matthew |
| contents | Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10743 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models Qiao, Zhuoran Ding, Feizhi Dresselhaus, Thomas Rosenfeld, Mia A. Han, Xiaotian Howell, Owen Iyengar, Aniketh Opalenski, Stephen Christensen, Anders S. Sirumalla, Sai Krishna Manby, Frederick R. Miller III, Thomas F. Welborn, Matthew Machine Learning Chemical Physics Biomolecules Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes. |
| title | NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models |
| topic | Machine Learning Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2412.10743 |