Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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.