Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening

This compressed file contains all datasets made for the validation of MUBDsyn.

• datasets_int_val: 17 cases in this folder are derived from MUBD for GPCRs. MUBDreal was made by MUBD-DecoyMaker2.0 and MUBDsyn was made by MUBD-DecoyMakersyn.
• datasets_ext_val_classical_VS: Five cases in this folder are derived from the shared cases of MUV and DUD-E. The active sets of MUV were taken as the input to make corresponding MUBD datasets. Files in SBVS are raw molecular docking results by smina.
• datasets_ext_val_SI_classical_VS: DeepCoy and TocoDecoy were used to make the datasets corresponding to the same five cases above. The data of DeepCoy was directly retrieved from DeepCoy resources at OPIG while topology decoys of TocoDecoy_9W were made based on the scripts provided at TocoDecoy GitHub Repository. Files in SBVS are raw molecular docking results by smina.
• datasets_ext_val_ML_VS: Ten cases in this folder are derived from NRLiSt-BDB. Corresponding MUBD datasets were made as described above.

All these datasets can be used for the reproduction of validation performed in the manuscript or to benchmark various virtual screening methods.