Abstract:We present KinetiDiff, a structure-based framework for de novo kinase inhibitor design that integrates a Geometry-Complete Diffusion Model with real-time AutoDock Vina gradient guidance. By injecting physics-based docking gradients into the diffusion denoising loop, KinetiDiff steers molecule generation toward high-affinity conformations for ACVR1 (ALK2), the causative kinase in Fibrodysplasia Ossificans Progressiva. From 10,000 diffusion samples, the framework produced 9,997 valid molecules. The best candidate achieved $-11.05$ kcal/mol (pKd = 8.10), a 19.2% improvement over the crystallographic reference. The top 100 candidates all exceed the reference, with 100% Lipinski compliance, median synthetic accessibility of 2.67, and internal diversity of 0.790. Systematic ablation across four guidance strategies--Vina-Direct (physics), HNN-Denovo (neural proxy), multi-objective, and unguided--demonstrates that real-time docking guidance dominates on all metrics. We evaluate HNN-Denovo as a computationally efficient alternative (60-fold speedup per step), revealing a domain-mismatch limitation (r = 0.224 correlation with Vina) that explains its inferior performance. These results establish gradient-guided geometric diffusion as a practical approach for generating potent, synthetically accessible inhibitors against rare-disease kinase targets.
| Comments: | 21 pages, 10 figures |
| Subjects: | Chemical Physics (physics.chem-ph); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.5.1; I.5.4; J.3 |
| Cite as: | arXiv:2604.20886 [physics.chem-ph] |
| (or arXiv:2604.20886v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2604.20886 arXiv-issued DOI via DataCite |
From: Aaryan Patel [view email]
[v1]
Fri, 17 Apr 2026 22:54:15 UTC (5,960 KB)
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