CACHE Challenge #1: Docking with GNINA Is All You Need.

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Structural biology

Background:

• The Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge aimed to evaluate structure-based virtual screening methods.
• Identifying novel ligands for targets with no prior known binders presents a significant challenge in drug discovery.
• 23 participants applied various computational approaches to identify hits for a specific protein target.

Purpose of the Study:

• To report the winning strategy for the first CACHE challenge.
• To detail an open-source computational workflow for hit identification.
• To validate the efficacy of pharmacophore search and molecular docking using only docking scores.

Main Methods:

• Employed pharmacophore searching and molecular docking for initial hit identification.
• Utilized docking scores exclusively for hit ranking and selection.
• Developed and applied an open-source workflow for hit expansion.

Main Results:

• The developed workflow successfully identified potent hit series.
• The best hit series achieved a tie for first place in the CACHE challenge evaluation.
• The approach demonstrated the effectiveness of relying solely on docking scores.

Conclusions:

• The presented computational workflow is a high-performing, open-source solution for hit identification.
• Pharmacophore search and molecular docking, when guided by docking scores, are effective strategies for novel target ligand discovery.
• The success in the CACHE challenge validates the utility of this approach in real-world drug discovery scenarios.