A two-dimensional chemical space map was constructed using two datasets (Figure ): a total of 543,491 virtual compound set (represented in green) was generated via Pocket Crafter workflow; a total of 1,101,793 compound set was screened at Novartis (represented in pink) to identify inhibitors of WDR5-MYC PPI using biochemical HTRF. Combining these datasets, this map was created using the R programming language, utilizing chemical-physical properties and ECFP-6 descriptors, with the assistance of the Rtsne function, t-distributed stochastic neighbor embedding (or t-SNE), a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. In order to illustrate the chemical space coverage connection between the two sets (Pocket2Mol and Novartis HTRF screen library) visually, we applied buffering to each data point in the t-SNE space as above, and then created circles with a fixed radius of r = 0.02. Subsequently, the circles were combined for both the Pocket2Mol (green) and Novartis HTRF (pink) sets. The light brown region represented the overlap of the chemical space as a function with radius of 0.02 in Figure . This chemical space map effectively demonstrates a broader coverage of the chemical landscape from Pocket2Mol, especially by the green "edge" area of the data point collection, highlighting the diversity of chemical scaffolds over Novartis library, even though this is an extremely diversified library by designing . Postprocessing Results with Two Layers of Filters. After the calculations to determine the chemical-physical properties of the generated compounds and data visualization in Figure , we applied a set of filters based on specific criteria (molecular weight, AlogP, Molecular Polar Surface Area, and Number of Rotatable Bonds) as depicted in Figure to 6D. As a result, 352,820 structures successfully met the selection criteria and advanced to the subsequent stage of analysis and evaluation. 352,820 novel virtual structures pose a significant challenge for early drug discovery, particularly concerning follow-up activities such as synthesis. This challenge is commonly encountered in generative chemistry. While generative chemistry garners increasing attention, the critical task of ensuring the synthetic feasibility of the generated molecules remains paramount. Bridging the gap between the innovative potential of generative chemistry and its practical application in synthesis is crucial for successful integration into drug discovery endeavors. To overcome this challenge, we conducted a comprehensive analysis on the 352,820 molecules that passed the chemical-physical property filters. These molecules were further evaluated based on their SAscore, QED score and GBVI/WSA ΔG score, employing specific cutoff values outlined in Table . The distribution of compounds by these hit calling parameters was thoroughly examined and visualized in Figure Following this subsequent hit calling filtering process, we successfully identified 9,531 virtual hits that exhibited favorable properties. These hits were then subjected to in silico SAR enrichment analysis, enabling a more profound exploration of their potential as promising candidates in the field of drug discovery. As we mentioned that Novartis library used for HTRF screen covers about 1.1 million druglike diverse compounds, so it is not surprising that many virtual hits from Pocket Crafter overlap with these structures in Novartis library, just showing as those dark brown dots in the light brown area of Figure . In this figure it also reveals an intriguing coincidence: two hit scaffolds, WM-662 and Compound 1 , previously identified from the experimental HTRF screen and published by us, are highlighted in red and aligned with the virtual hits identified through the Pocket Crafter workflow. This "overlapped hit" observation proves the great potential of this workflow to generate hit compounds virtually with high relevance to the target, as the pocket binders. More encouragingly, this workflow can go beyond our diverse library and generate novel "hits", as many dark brown dots are outside of the overlapped area, which means they are distinct structures not covered by the 1.1 million diverse compound collection.