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Ligand Binding Sites02:40

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Updated: Jul 3, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Reinforcement learning for in silico determination of adsorbate-substrate structures.

Maicon Pierre Lourenço1, Jiří Hostaš2,3, Colin Bellinger3

  • 1Departamento de Química e Física-Centro de Ciências Exatas, Naturais e da Saúde-CCENS-Universidade Federal do Espírito Santo, Alegre, Brasil.

Journal of Computational Chemistry
|February 15, 2024
PubMed
Summary

This study introduces a reinforcement learning (RL) method for determining adsorbate@substrate structures. The approach uses Q-learning and artificial intelligence to efficiently explore and predict stable configurations in materials science.

Keywords:
DFTDFTBadsorptionfunctional materialsreinforcement learningtransfer‐learning

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Area of Science:

  • Computational chemistry
  • Materials science
  • Artificial intelligence

Background:

  • Reinforcement learning (RL) has achieved state-of-the-art results in artificial intelligence, notably after breakthroughs like AlphaGo.
  • Determining the stable structures of adsorbates on substrates is crucial for understanding surface interactions and designing new materials.
  • Traditional methods for structural determination can be computationally expensive and time-consuming.

Purpose of the Study:

  • To present a novel RL-based method for the in silico structural determination of adsorbate@substrate models.
  • To develop and implement this RL method within the RLMaterial software for materials design.
  • To demonstrate the applicability of the method across diverse chemical systems.

Main Methods:

  • A Q-learning based reinforcement learning approach was employed to navigate the energy landscape of adsorbate@substrate interactions.
  • The RL agent performs actions (translations, rotations) to minimize energy, guided by a learned policy.
  • RLMaterial software interfaces with computational chemistry codes (deMon2k, DFTB+, ORCA, Quantum Espresso) for energy calculations.
  • Artificial neural networks and gradient boosting regression were used to approximate the Q-matrix for efficient decision-making.
  • Density Functional Tight Binding (DFTB) and Density Functional Theory (DFT) were utilized for energy computations.

Main Results:

  • The RL method successfully determined the structures of glycine and 2-amino-acetaldehyde on a boron nitride monolayer.
  • It also elucidated host-guest interactions between phenylboronic acid and β-cyclodextrin, and ammonia on naphthalene.
  • The use of machine learning techniques (ANN, gradient boosting) improved the efficiency of the Q-matrix approximation.
  • A transfer-learning protocol was successfully developed, enabling knowledge transfer between different chemical systems and computational levels.

Conclusions:

  • The developed RL method offers an efficient and effective approach for the structural determination of adsorbate@substrate systems.
  • RLMaterial provides a versatile platform for accelerating materials discovery through AI-driven simulations.
  • The transfer-learning capability enhances the adaptability and predictive power of RL in computational chemistry.