With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds-to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol, can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.

Summary Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project. Availability and Implementation DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI Supplementary information Supplementary data are available at Bioinformatics online.

distal ones (p <0.0001). The total improvement in curve was also variably associated with change in POMC, with those experiencing greater improvements noted to have larger change in POMC (p <0.05 for changes in composite curvature and p[0.07 for percent change from baseline). These findings would suggest significant evolution of the POMC during a course of CCH, particularly among men with mid shaft or proximal curves and among those with greater curve improvements. Findings would support a role for repeat curvature assessments prior to each series of CCH injections.CONCLUSIONS: Based on data from a prospective, twoinstitution series of PD men undergoing CCH injections, the POMC changes by !1 cm in the majority of men. Given the small volume injected with each CCH series, the clinical relevance of these findings and their impact on injector protocols merit further investigation.