Abstract A024: FastBindRank, a novel, scalable method for high-fidelity virtual screening of ultra-large chemical libraries idendtifies novel HDAC11 inhibitors
TL;DR
FastBindRank is presented, a distillation-based framework that transfers the predictive power of a high-accuracy structure-based model (Boltz-2) into a computationally efficient sequence-based surrogate and can identify functionally active novel compounds from ultra-large virtual screening.
Abstract
Accurate structure-based virtual screening of ultra-large chemical libraries remains challenging. Existing approaches rely on either lower-fidelity scoring functions or sampling-based strategies, which can limit predictive accuracy and introduce biases in the exploration of chemical space. Here, we present FastBindRank, a distillation-based framework that transfers the predictive power of a high-accuracy structure-based model (Boltz-2) into a computationally efficient sequence-based surrogate. By training on ∼1% of the 122-million-compound PubChem library, FastBindRank enables high-fidelity screening at scale. We applied this framework to histone deacetylase 11 (HDAC11), the sole class IV member of the histone deacetylase family and epigenetic regulator implicated in tumor progression and therapy resistance, yet remains chemically underexplored with relatively few inhibitors available. Compared with a random background (N = 1.85 million), FastBindRank effectively enriched high-confidence binders, with higher predicted binding probabilities (Cliff’s δ = 0.97) and lower predicted log10(IC50) values (Cliff’s δ = −0.75). Re-scoring and physicochemical filtering yielded 1,262 high-confidence candidates and 528 structurally diverse representatives. Under a comparable computational budget, our approach achieved a 74-fold increase in hit rate and over a 30-fold increase in discovery yield over direct subset-based screening. To interpret the structural patterns underlying model predictions, SHapley Additive exPlanations (SHAP) analysis was performed on the top-ranked candidates (N = 500) which revealed a subset of Morgan fingerprint bits with high contributions, indicating that model predictions are driven by specific local chemical environments The framework’s predictive accuracy was experimentally validated for two novel compounds using an in vitro HDAC11 enzyme activity assay with panobinostat and fimepinostat (both FDA approved pan-HDAC inhibitors) as positive controls. Both novel compounds showed HDAC11 inhibitory activity similar to or higher than the positive controls. The IC50 values were 1.3 µM and 14.8 µM, respectively, which compare favorably with Panobinostat and Fimepinostat that had IC50 values of 20.8 µM and 3.3 µM, respectively. These results provide experimental support for the predictive capability of FastBindRank and demonstrate that large-scale structure-guided prioritization can identify functionally active novel compounds, offering a practical path for candidate discovery from ultra-large virtual screening. Jiawei Dai, Yueyue Wang, Naing Lin Shan, Marco Mariani, Zimeng Yu, Qin Yan, Lalit Golani, Yulia Surovtseva, William Lee, Lajos Pusztai. FastBindRank, a novel, scalable method for high-fidelity virtual screening of ultra-large chemical libraries idendtifies novel HDAC11 inhibitors [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A024.