--- license: cc-by-sa-4.0 language: - en size_categories: - 100K 4.9 β†’ active**, otherwise inactive. The 4.9 threshold was chosen empirically to maximise per-target coverage while maintaining assay reliability. Compounds with conflicting labels (e.g. active under one assay, inactive under another) are resolved as **active** to avoid penalising true positives discovered later. #### Who are the annotators? There are no human annotators specific to TopU-LBVS. All labels are derived programmatically from ChEMBL bioactivity records via the threshold rule above. The TopU hard-decoy selection itself is fully automated via a constrained genetic algorithm (see Appendix A.2 of the paper). ### Personal and Sensitive Information **None.** The dataset consists of small-molecule chemical structures (SMILES), ChEMBL identifiers, and binary activity labels against protein targets. No human-subject data, no personally identifiable information, no patient records. --- ## Considerations for Using the Data ### Social Impact of Dataset TopU-LBVS is intended to improve the rigour and reproducibility of computational drug discovery research. By exposing the gap between random-decoy and hard-negative screening performance, the benchmark may help reduce overoptimistic claims about molecular machine-learning models and encourage methods that work under realistic early-stage discovery conditions. Improved virtual screening could reduce the time and cost of identifying lead compounds for diseases with unmet medical need. **Dual-use risk note**: improved virtual-screening methods could in principle be misused to prioritise harmful bioactive compounds. TopU-LBVS, however, is a retrospective evaluation resource only β€” it provides no generative design capability, no synthesis routes, no prospective experimental validation, and no compound-purchase pathway. The release is focused on transparent evaluation, not molecule generation or deployment. ### Discussion of Biases - **Target-class imbalance.** The 93 targets are not class-balanced. Kinases, GPCRs, and miscellaneous enzymes contribute the most tasks; ion channels, proteases, and nuclear receptors fewer. This reflects the structure of public bioactivity coverage in ChEMBL rather than a deliberate sampling choice. Class-averaged metrics (reported in the paper) partially compensate. - **Chemical-space bias.** ChEMBL skews toward chemotypes that have already been explored in the medicinal-chemistry literature. Compounds from underrepresented chemical spaces (covalent inhibitors, macrocycles, PROTACs, PPI modulators) are sparse. - **Threshold-binarisation noise.** Fixed pChEMBL > 4.9 cutoff does not capture experimental uncertainty, assay context, or graded potency. A compound at pChEMBL = 4.95 is labelled identically to one at 9.0. - **Inactive labels are noisy.** Inactives in ChEMBL are often "tested and not active in this assay" rather than confirmed inactive across all conditions, so some decoys may be mis-labelled actives. The TopU hard-decoy selection process partially mitigates this by selecting decoys from a curated inactive pool, but cannot remove the noise entirely. ### Other Known Limitations - **Retrospective and 2D ligand-based.** No 3D protein structure information, docking scores, conformer ensembles, or prospective experimental validation. The benchmark does not evaluate structure-based screening or hybrid ligand–protein methods. - **Adversarial by design.** TopU decoys emphasise worst-case structural similarity to actives, so absolute EF values are systematically lower than on standard benchmarks. This is intentional and exposes failure modes β€” but it may overestimate difficulty relative to early-exploration screening with diverse libraries. - **ChEMBL-only.** Conclusions may not directly generalise to underrepresented target families, proprietary chemical spaces, or non-ChEMBL data sources. - **Tier 1 variance.** Few-shot tasks with fewer than 50 actives have high per-target metric variance. Class-level and overall averages are more reliable than individual-target EF estimates for these tasks. A full discussion is in Appendix F of the paper. --- ## Additional Information ### Dataset Curators - **Surbhi Kumar** β€” UT Dallas, Mathematical Sciences (co-first author) - **Yuhe Zhou** β€” National Institute of Biological Sciences, Beijing (co-first author) - **Varun Shiralkar** β€” UT Dallas, Computer Science - **Niu Huang** β€” National Institute of Biological Sciences, Beijing (co-senior author) - **Baris Coskunuzer** β€” UT Dallas, Mathematical Sciences (co-senior author) ### Licensing Information The TopU-LBVS dataset is released under **CC-BY-SA-4.0**. The companion code (https://github.com/topu-benchmark/topu-lbvs) is released under the **MIT License**. ChEMBL is released under CC-BY-SA-3.0. The curated TopU-LBVS data inherits a compatible CC-BY-SA-4.0 license. Users redistributing the data or derived works must preserve the CC-BY-SA terms and provide attribution to both ChEMBL and this dataset. ### Citation Information If you use TopU-LBVS in your work, please cite both this benchmark and ChEMBL: ```bibtex @unpublished{topu_lbvs_2026, title = {TopU-LBVS: A Realistic Multi-Target Benchmark for Ligand-Based Virtual Screening}, author = {Kumar, Surbhi and Zhou, Yuhe and Shiralkar, Varun and Huang, Niu and Coskunuzer, Baris}, year = {2026}, note = {Under review at NeurIPS 2026 Datasets and Benchmarks Track} } @article{zdrazil2024chembl, title = {The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods}, author = {Zdrazil, Barbara and others}, journal = {Nucleic Acids Research}, year = {2024} } ``` ### Contributions We thank the ChEMBL team at EMBL-EBI for maintaining the underlying bioactivity database, and the broader medicinal-chemistry and cheminformatics community whose data this benchmark builds on. We thank the authors of RDKit, PyTorch Geometric, Chemprop, and MolFormer for the open-source tools used in the reference baselines. For issues, questions, and discussion, please use the [GitHub issue tracker](https://github.com/topu-benchmark/topu-lbvs/issues).