autodock-gpu 1.5.350 program  (AUTODOCK GmbH)

Journal: Chemical Science

Article Title: Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules †

doi: 10.1039/d1sc05579h

Figure Lengend Snippet: Database size, number of iterations and number of docked molecules for each program used in the DD screen

Article Snippet: The conventional docking of a library of this size would require extraordinary resources, and up to 10 years of running time with the fastest Autodock GPU program implemented on 250 GPU units.

Techniques:

Journal: Chemical Science

Article Title: Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules †

doi: 10.1039/d1sc05579h

Figure Lengend Snippet: Computational hit selection strategies. In arm 1, molecules were ranked by average docking scores (1.0_K) and then serially filtered by standard deviation (std) of the docking scores (L), consensus docking (M), pharmacophore (ph4) modelling (N), and best-first clustering based on average scores (O), and then re-ranked using tree-based clustering based on five scores (P). Two different combinations of std values and numbers of consensus programs were used (1.1 and 1.2 branch). In arm 2, top 100 000 molecules from each individual docking result were best-first clustered and the same process was repeated, using 1 std cutoff (2.0_L). Compounds in the heatmaps are colored by their individual, original ranks from docking, and the number of retained compounds is reported below the name of the strategy. Correlation values of single docking ranks improved with the filtering procedure, although ranking correlation was less significant when best-first clustering was applied to the outcomes of single docking programs, as illustrated by the Spearman matrices below heatmaps. A: Autodock GPU, G: Glide SP, F: FRED, I: ICM, Q: QuickVina2.

Article Snippet: The conventional docking of a library of this size would require extraordinary resources, and up to 10 years of running time with the fastest Autodock GPU program implemented on 250 GPU units.

Techniques: Selection, Standard Deviation

Journal: Chemical Science

Article Title: Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules †

doi: 10.1039/d1sc05579h

Figure Lengend Snippet: Tanimoto similarity trees of selected top-100 compounds from individual docking programs, where the top-five clusters are highlighted by different colors in each dendrogram. (a) Autodock GPU, (b) Glide SP, (c) FRED, (d) ICM and (e) QuickVina2.

Article Snippet: The conventional docking of a library of this size would require extraordinary resources, and up to 10 years of running time with the fastest Autodock GPU program implemented on 250 GPU units.

Techniques:

Journal: Chemical Science

Article Title: Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules †

doi: 10.1039/d1sc05579h

Figure Lengend Snippet: Tanimoto similarity distributions of top-100 compounds from individual docking programs before (in blue) and after chemical diversity selection (in orange). (a) Autodock GPU, (b) Glide SP, (c) FRED, (d) ICM and (e) QuickVina2.

Article Snippet: The conventional docking of a library of this size would require extraordinary resources, and up to 10 years of running time with the fastest Autodock GPU program implemented on 250 GPU units.

Techniques: Selection