Paper List
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Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
This paper addresses the fundamental limitation of current AI-assisted scientific research by enabling truly autonomous, decentralized investigation w...
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D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing
This paper addresses the fundamental scalability bottleneck in LLM agentic memory systems: the O(N²) computational complexity and unbounded API token ...
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Countershading coloration in blue shark skin emerges from hierarchically organized and spatially tuned photonic architectures inside skin denticles
This paper solves the core problem of how blue sharks achieve their striking dorsoventral countershading camouflage, revealing that coloration origina...
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Human-like Object Grouping in Self-supervised Vision Transformers
This paper addresses the core challenge of quantifying how well self-supervised vision models capture human-like object grouping in natural scenes, br...
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Hierarchical pp-Adic Framework for Gene Regulatory Networks: Theory and Stability Analysis
This paper addresses the core challenge of mathematically capturing the inherent hierarchical organization and multi-scale stability of gene regulator...
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Towards unified brain-to-text decoding across speech production and perception
This paper addresses the core challenge of developing a unified brain-to-text decoding framework that works across both speech production and percepti...
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Dual-Laws Model for a theory of artificial consciousness
This paper addresses the core challenge of developing a comprehensive, testable theory of consciousness that bridges biological and artificial systems...
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Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
This work addresses the core challenge of determining optimal pulse timing and intensity for desynchronizing pathological neural oscillations when the...
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.