# De novo protein–ligand design including protein flexibility and conformational adaptation

## TL;DR

This paper introduces AI-MCLig, a new method for designing drug compounds that accounts for protein flexibility, improving the accuracy of drug-target binding predictions.

## Contribution

AI-MCLig is a novel approach that incorporates protein flexibility and conformational adaptation during de novo ligand design.

## Key findings

- AI-MCLig generates potential ligands with binding scores comparable to known binders.
- The method allows for protein adaptation during compound design through Monte Carlo simulations.
- Test results on four targets show promising performance using multiple scoring schemes.

## Abstract

The rational design of chemical compounds that bind to a desired protein target molecule is a major goal of drug discovery. Most current molecular docking but also fragment-based buildup or machine learning-based generative drug design approaches employ a rigid protein target structure.

Based on recent progress in predicting protein structures and complexes with chemical compounds, we have designed an approach, AI-MCLig, to optimize a chemical compound bound to a fully flexible and conformationally adaptable protein binding region. During a Monte Carlo (MC)-type simulation to randomly change a chemical compound, the target protein–compound complex is completely rebuilt at every MC step using the Chai-1 protein structure prediction program. Besides compound flexibility it allows the protein to adapt to the chemically changing compound. MC protocols based on atom-/bond-type changes or based on combining larger chemical fragments have been tested. Simulations on four test targets resulted in potential ligands that show very good binding scores comparable to experimentally known binders using several different scoring schemes. The MC-based compound design approach is complementary to existing approaches and could help for the rapid design of putative binders including induced fit of the protein target.

Datasets, examples, and source code are available on our public GitHub repository https://github.com/JakobAgamia/AI-MCLig and on Zenodo at https://doi.org/10.5281/zenodo.17800140.