Automatic tailoring and transplanting: a practical method that makes virtual screening more useful.

Area of Science:

• Computational Chemistry
• Drug Design
• Medicinal Chemistry

Background:

• Virtual screening is crucial for lead discovery in structure-based drug design.
• Lead optimization typically employs separate computational methods like de novo design.
• A gap exists in efficiently leveraging virtual screening data for lead optimization.

Purpose of the Study:

• To introduce Automatic Tailoring and Transplanting (AutoT&T), a novel method for lead optimization.
• To demonstrate AutoT&T's capability in utilizing virtual screening hits for generating optimized ligands.
• To compare AutoT&T's performance against established de novo design methods.

Main Methods:

• AutoT&T detects and transplants suitable fragments from virtual screening hits onto lead compounds.
• The method incorporates binding affinity, synthetic feasibility, and drug-likeness in design selection.
• The program was validated using three target proteins: p38 MAP kinase, PPAR-α, and Mcl-1.

Main Results:

• AutoT&T generated known or similar inhibitors with improved potency for p38 MAP kinase and PPAR-α.
• Novel ligand molecules were designed from scratch for Mcl-1 using AutoT&T.
• AutoT&T produced more structurally diverse and higher-scoring compounds than LUDI and EA-Inventor.
• The AutoT&T method demonstrated several-fold greater efficiency compared to LUDI and EA-Inventor.

Conclusions:

• AutoT&T offers technical advantages over traditional de novo design methods.
• This approach effectively bridges virtual screening from lead discovery to lead optimization.
• AutoT&T serves as a valuable tool for researchers in drug design and optimization.