Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Binding Free Energies without Alchemy
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill | Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill
30秒速读
IN SHORT: This paper addresses the core bottleneck of computational expense in Absolute Binding Free Energy calculations by eliminating the need for numerous alchemical intermediate simulations, reducing per-ligand simulation cost by up to 26x.
核心创新
- Methodology Introduces Direct Binding Free Energy (DBFE), a novel end-state ABFE method that requires only three simulations (receptor-only, ligand-only, and complex) without alchemical intermediates.
- Methodology Employs a combinatorial sampling strategy using KD-trees for fast steric clash detection, enabling efficient estimation of conformational entropy from precomputed simulations.
- Methodology Demonstrates a 26x reduction in per-ligand simulation cost compared to double decoupling methods in virtual screening contexts through amortization of receptor simulations.
主要结论
- DBFE achieved Pearson correlation r=0.58 on host-guest systems, outperforming OBC2 double decoupling (r=0.48) and demonstrating the importance of conformational entropy correction for these systems.
- On protein-ligand benchmarks, DBFE achieved r=0.65, slightly worse than OBC2 MM/GBSA (r=0.71), suggesting conformational entropy estimation introduces noise for complex protein systems.
- The performance gap between implicit solvent methods (DBFE/OBC2 DD r=0.65-0.73) and explicit solvent TIP3P DD (r=0.88) indicates that improving implicit solvent models would yield greater accuracy gains than improving free energy estimators.
摘要: Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by the need for many simulations of alchemically modified intermediate states. We propose Direct Binding Free Energy (DBFE), an end-state ABFE method in implicit solvent that requires no alchemical intermediates. DBFE outperforms OBC2 double decoupling on a host-guest benchmark and performs comparably to OBC2 MM/GBSA on a protein-ligand benchmark. Since receptor and ligand simulations can be precomputed and amortized across compounds, DBFE requires only one complex simulation per ligand compared to the many lambda windows needed for double decoupling, making it a promising candidate for virtual screening workflows. We publicly release the code for this method at https://github.com/molecularmodelinglab/dbfe.