arXiv subjects

Jiabao Brad Wang

Publications and source records attributed to Jiabao Brad Wang.

4 recordsLinked to original sources

arXiv2026

Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization

Automated algorithm selection for continuous black-box optimization depends on what information is acquired from a problem under a limited probing budget and how that information is represented. We introduce a geometric probing framework that samples multi-scale two-dimensional restrictions across location, orientation, and scale, and encodes their normalized objective-value maps with validity-aware convolutional processing and permutation-invariant aggregation. We compare our method with classical ELA and Deep-ELA under matched budgets, within-problem and problem-level transfer, fusion, representation, and budget analyses. We further disentangle probe acquisition from probe processing by controlled ablation. The results show that the proposed visual representation exposes solver-performance information complementary to ELA-family features and retains a relative advantage for relative expected runtime under problem-level transfer, while greater coverage, feature richness, or predictive accessibility alone does not guarantee better selection.

cs.LG↗

arXiv2026

Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking

Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes. MolAS is a lightweight algorithm-selection model that predicts per-algorithm performance from pretrained protein and ligand embeddings using attentional pooling and a shallow residual decoder. With hundreds to a few thousand labelled complexes, MolAS achieves up to a 15 percentage-point absolute improvement over the single-best solver (SBS) and closes 17--66\% of the Virtual Best Solver (VBS)--SBS gap across five docking benchmarks. Analyses of selection frequencies, margin-conditioned reliability, and benchmark-level oracle structure indicate that MolAS is most effective when the workflow-defined oracle landscape has low winner entropy and a reasonably separable top-solver region, but degrades under protocol mismatch that shifts solver rankings and changes the induced labels. These results suggest that, in the evaluated regime, robustness is limited less by representational capacity than by workflow- and protocol-induced instability in solver hierarchies, positioning MolAS as an in-domain selector for fixed pipelines and as a diagnostic tool for assessing when docking algorithm selection is well-posed.

q-bio.QM↗

arXiv2025

MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking

Molecular docking is a core tool in drug discovery for predicting ligand-target interactions. Despite the availability of diverse search-based and machine learning approaches, no single docking algorithm consistently dominates, as performance varies by context. To overcome this challenge, algorithm selection frameworks such as GNNAS-Dock, built on graph neural networks, have been proposed. This study introduces an enhanced system, MC-GNNAS-Dock, with three key advances. First, a multi-criteria evaluation integrates binding-pose accuracy (RMSD) with validity checks from PoseBusters, offering a more rigorous assessment. Second, architectural refinements by inclusion of residual connections strengthen predictive robustness. Third, rank-aware loss functions are incorporated to sharpen rank learning. Extensive experiments are performed on a curated dataset containing approximately 3200 protein-ligand complexes from PDBBind. MC-GNNAS-Dock demonstrates consistently superior performance, achieving up to 5.4% (3.4%) gains under composite criteria of RMSD below 1Å (2Å) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.

cs.AI↗

arXiv2025

Power-Linear Polar Directional Fields

We introduce a novel method for directional-field design on meshes, enabling users to specify singularities at any location on a mesh. Our method uses a piecewise power-linear representation for phase and scale, offering precise control over field topology. The resulting fields are smooth and accommodate any singularity index and field symmetry. With this representation, we mitigate the artifacts caused by coarse or uneven meshes. We showcase our approach on meshes with diverse topologies and triangle qualities.

cs.GR↗