Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Neuroscience

Background:

• Fragment-based drug discovery (FBDD) is a successful strategy in drug development, leading to approved therapeutics.
• Kinases are important drug targets, with BRAF inhibitors like vemurafenib demonstrating FBDD success.
• Protein kinase CK1δ is a target for neurodegenerative diseases including Alzheimer's, Parkinson's, and ALS.

Purpose of the Study:

• To modify the Autogrow software for enhanced fragment-based drug discovery (FBDD).
• To develop a novel scoring function based on interaction fingerprints for improved compound design.
• To evaluate the performance of the modified Autogrow protocol in generating CK1δ inhibitors.

Main Methods:

• Utilized a modified version of the open-source Autogrow software.
• Implemented a custom scoring function incorporating interaction fingerprint similarity to a crystal reference.
• Performed de novo and lead-optimization runs using the enhanced protocol.

Main Results:

• The modified Autogrow protocol was evaluated for its ability to generate compounds similar to known CK1δ inhibitors.
• Performance was assessed based on predicted binding mode and electrostatic/shape similarity.
• Comparison was made against the standard Autogrow protocol's results.

Conclusions:

• The developed fingerprint-based scoring function shows potential for improving FBDD.
• The modified Autogrow protocol can aid in the design of kinase inhibitors for neurodegenerative diseases.
• This approach offers a valuable tool for computational drug design and lead optimization.