Skip to content

BOLTZREFPAPERS.md

Latest commit

 

History

History
126 lines (64 loc) · 7.46 KB

BOLTZREFPAPERS.md

File metadata and controls

126 lines (64 loc) · 7.46 KB
Raw

Boltz-1 Reference Papers List

Core Paper

Foundation Models

  • 2. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024). https://doi.org/10.1038/s41586-024-07487-w

  • 3. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

  • 4. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

Diffusion Models for Structure

  • 5. Ho, J., Jain, A. & Abbeel, P. Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems 33, 6840–6851 (2020).

  • 6. Song, Y. et al. Score-Based Generative Modeling through Stochastic Differential Equations. International Conference on Learning Representations (2021).

  • 7. Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

  • 8. Yim, J. et al. SE(3) diffusion model with application to protein backbone generation. International Conference on Machine Learning, 40001–40039 (2023).

  • 9. Trippe, B. L. et al. Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. International Conference on Learning Representations (2023).

Transformer Architecture

  • 10. Vaswani, A. et al. Attention Is All You Need. Advances in Neural Information Processing Systems 30, 5998–6008 (2017).

  • 11. Peebles, W. & Xie, S. Scalable Diffusion Models with Transformers. International Conference on Computer Vision, 4195–4205 (2023).

  • 12. Dao, T. et al. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. Advances in Neural Information Processing Systems 35, 16344–16359 (2022).

  • 13. Dao, T. FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. arXiv:2307.08691 (2023).

Geometric Deep Learning

  • 14. Satorras, V. G., Hoogeboom, E. & Welling, M. E(n) Equivariant Graph Neural Networks. International Conference on Machine Learning, 9323–9332 (2021).

  • 15. Jing, B. et al. Equivariant Graph Neural Networks for 3D Macromolecular Structure. arXiv:2106.03843 (2021).

  • 16. Hoogeboom, E. et al. Equivariant Diffusion for Molecule Generation in 3D. International Conference on Machine Learning, 8867–8887 (2022).

Protein Language Models

  • 17. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. PNAS 118, e2016239118 (2021).

  • 18. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

  • 19. Rao, R. et al. MSA Transformer. International Conference on Machine Learning, 8844–8856 (2021).

MSA & Sequence Alignment

  • 20. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology 35, 1026–1028 (2017).

  • 21. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nature Methods 19, 679–682 (2022).

  • 22. Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nature Methods 9, 173–175 (2012).

  • 23. Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).

Small Molecule Representation

  • 24. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 28, 31–36 (1988).

  • 25. Landrum, G. RDKit: Open-source cheminformatics. https://www.rdkit.org/

  • 26. Riniker, S. & Landrum, G. A. Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation. Journal of Chemical Information and Modeling 55, 2562–2574 (2015).

Molecular Docking

  • 27. Corso, G. et al. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. International Conference on Learning Representations (2023).

  • 28. Stärk, H. et al. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. International Conference on Machine Learning, 20503–20521 (2022).

  • 29. Lu, W. et al. TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction. NeurIPS 35, 7236–7249 (2022).

  • 30. McNutt, A. T. et al. GNINA 1.0: molecular docking with deep learning. Journal of Cheminformatics 13, 43 (2021).

Confidence Metrics

  • 31. Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: A local superposition-free score for comparing protein structures and models. Bioinformatics 29, 2722–2728 (2013).

  • 32. Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).

  • 33. Xu, J. & Zhang, Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26, 889–895 (2010).

Databases

  • 34. wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Research 47, D520–D528 (2019).

  • 35. UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research 51, D523–D531 (2023).

  • 36. Kim, S. et al. PubChem 2023 update. Nucleic Acids Research 51, D483–D492 (2023).

  • 37. Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Research 47, D930–D940 (2019).

Training Infrastructure

  • 38. Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 32, 8024–8035 (2019).

  • 39. Loshchilov, I. & Hutter, F. Decoupled Weight Decay Regularization. International Conference on Learning Representations (2019).

  • 40. Shoeybi, M. et al. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. arXiv:1909.08053 (2019).

Protein-Ligand Interaction

  • 41. Buttenschoen, M., Morris, G. M. & Deane, C. M. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science 15, 3130–3139 (2024).

  • 42. Friesner, R. A. et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry 47, 1739–1749 (2004).

Open Source Philosophy

  • 43. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nature Methods 19, 679–682 (2022).

  • 44. Ahdritz, G. et al. OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods (2024).

Benchmarking

  • 45. Kryshtafovych, A. et al. Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XV. Proteins (2024).

  • 46. Su, M. et al. Comparative Assessment of Scoring Functions: The CASF-2016 Update. Journal of Chemical Information and Modeling 59, 895–913 (2019).