Structural feature-driven pattern analysis for multitarget modulator landscapes.

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Pharmacology

Background:

• Multitargeting small molecules offer potential therapeutic advantages by addressing multiple targets.
• Understanding multitargeting features is crucial for efficient drug selection, especially in large-scale virtual screening.
• Existing large-scale multitarget modulator landscapes are limited.

Purpose of the Study:

• To implement a feature-driven computer-aided pattern analysis (C@PA) for extracting molecular-structural features of inhibitors.
• To expand the known multitarget modulator landscape for ATP-binding cassette (ABC) transporters.
• To discover novel pan-ABC transporter inhibitors with distinct activity profiles.

Main Methods:

• Implemented a feature-driven computer-aided pattern analysis (C@PA).
• Extracted molecular-structural features of inhibitors targeting the ATP-binding cassette (ABC) transporter family.
• Validated prediction capabilities through biological experiments.

Main Results:

• Identified novel molecular-structural features for multitarget modulators.
• Successfully expanded the multitarget modulator landscape for pan-ABC transporter inhibitors.
• Discovered novel pan-ABC transporter inhibitors with unique inhibitory activity profiles, confirming prediction capabilities.

Conclusions:

• The developed C@PA method effectively identifies novel multitargeting features.
• The expanded landscape provides valuable insights for discovering pan-ABC transporter inhibitors.
• This approach facilitates the identification of high-quality compound collections for drug discovery.