Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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.