Advanced Computational Models for Predicting Molecular Interactions

Year : 2024 | Volume : 02 | Issue : 01 | Page : 8 13
    By

    Neha Sahu,

  • Rizwan Arif,

  1. Research Scholar, Department of Chemistry, Lingaya’s Vidyapeeth, Faridabad, Haryana, India
  2. Assistant Professor, Department of Chemistry, Lingaya’s Vidyapeeth, Faridabad, Haryana, India

Abstract

Understanding molecular interactions is essential for a number of disciplines, including biochemistry, materials science, and medication development. Traditional experimental methods, while accurate, are often time-consuming and expensive. Advanced computational models have emerged as powerful tools to predict molecular interactions efficiently. In order to predict the behavior and interactions of molecules at the atomic and subatomic levels, this paper reviews the most recent developments in computational techniques, such as machine learning algorithms, quantum mechanics/molecular mechanics (QM/MM) methods, and molecular dynamics (MD) simulations. MD simulations allow for the detection of transitional states and conformational changes, offering precise insights into the dynamic behavior of molecular systems throughout time. By fusing the efficiency of molecular mechanics with the accuracy of quantum mechanics, QM/MM approaches provide a balanced approach that enables high precision investigation of massive biomolecular systems. Machine learning algorithms, leveraging vast amounts of data, can predict molecular interactions with remarkable speed and accuracy, often surpassing traditional methods in terms of scalability and versatility. This paper also discusses the integration of these computational models with experimental data to enhance their predictive power and reliability. Case studies from recent research are presented to illustrate the application of these models in predicting drug-receptor interactions, protein-ligand binding affinities, and material properties. The challenges and future directions in the field, such as the need for more accurate force fields, better algorithms for data handling, and increased computational power, are also explored.

Keywords: Computational models, drug-receptor interactions, machine learning algorithms, molecular dynamics simulations, molecular interactions, quantum mechanics/molecular mechanics (QM/MM)

[This article belongs to International Journal of Advance in Molecular Engineering ]

How to cite this article:
Neha Sahu, Rizwan Arif. Advanced Computational Models for Predicting Molecular Interactions. International Journal of Advance in Molecular Engineering. 2024; 02(01):8-13.
How to cite this URL:
Neha Sahu, Rizwan Arif. Advanced Computational Models for Predicting Molecular Interactions. International Journal of Advance in Molecular Engineering. 2024; 02(01):8-13. Available from: https://journals.stmjournals.com/ijame/article=2024/view=179538


References

  1. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A. Highly accurate protein structure prediction with AlphaFold. nature. 2021 Aug;596(7873):583-9.
  2. Kreitz J, Friedrich MJ, Guru A, Lash B, Saito M, Macrae RK, Zhang F. Programmable protein delivery with a bacterial contractile injection system. Nature. 2023 Apr 13;616(7956):357-64.
  3. Lim Y, Tamayo-Orrego L, Schmid E, Tarnauskaite Z, Kochenova OV, Gruar R, Muramatsu S, Lynch L, Schlie AV, Carroll PL, Chistol G. In silico protein interaction screening uncovers DONSON’s role in replication initiation. Science. 2023 Aug 17;381(6664):eadi3448.
  4. Mosalaganti S, Obarska-Kosinska A, Siggel M, Taniguchi R, Turoňová B, Zimmerli CE, Buczak K, Schmidt FH, Margiotta E, Mackmull MT, Hagen WJ. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science. 2022 Jun 10;376(6598):eabm9506.
  5. Anand N, Achim T. Protein structure and sequence generation with equivariant denoising diffusion probabilistic models. arXiv preprint arXiv:2205.15019. 2022 May 26.
  6. Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduction and Targeted Therapy. 2023 Mar 14;8(1):115.
  7. Protein complex prediction with AlphaFold-Multimer Židek, A. AlphaFold v.2.3.0 Technical Note. GitHub https://github.com/google-deepmind/alphafold/blob/main/docs/technical_note_v2.3.0.md (2022).
  8. Isert C, Atz K, Schneider G. Structure-based drug design with geometric deep learning. Current Opinion in Structural Biology. 2023 Apr 1;79:102548.
  9. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023 Mar 17;379(6637):1123-30.
  10. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, Millán C. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021 Aug 20;373(6557):871-6.
  11. Wu R, Ding F, Wang R, Shen R, Zhang X, Luo S, Su C, Wu Z, Xie Q, Berger B, Ma J. High-resolution de novo structure prediction from primary sequence. BioRxiv. 2022 Jul 22:2022-07.
  12. Bryant P, Pozzati G, Elofsson A. Improved prediction of protein-protein interactions using AlphaFold2. Nature communications. 2022 Mar 10;13(1):1265.
  13. Qiao Z, Nie W, Vahdat A, Miller III TF, Anandkumar A. State-specific protein–ligand complex structure prediction with a multiscale deep generative model. Nature Machine Intelligence. 2024 Feb;6(2):195-208.
  14. Nakata S, Mori Y, Tanaka S. End-to-end protein–ligand complex structure generation with diffusion-based generative models. BMC bioinformatics. 2023 Jun 5;24(1):233.
  15. Baek M, McHugh R, Anishchenko I, Jiang H, Baker D, DiMaio F. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nature methods. 2024 Jan;21(1):117-21.
  16. Townshend RJ, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Geometric deep learning of RNA structure. Science. 2021 Aug 27;373(6558):1047-51.
  17. Jiang D, Hsieh CY, Wu Z, Kang Y, Wang J, Wang E, Liao B, Shen C, Xu L, Wu J, Cao D. Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions. Journal of medicinal chemistry. 2021 Dec 8;64(24):18209-32.
  18. Jiang H, Wang J, Cong W, Huang Y, Ramezani M, Sarma A, Dokholyan NV, Mahdavi M, Kandemir MT. Predicting protein–ligand docking structure with graph neural network. Journal of chemical information and modeling. 2022 Jun 14;62(12):2923-32.
Regular Issue Subscription Review Article
Volume 02
Issue 01
Received 02/08/2024
Accepted 30/08/2024
Published 23/10/2024
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