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GANDALF: A Prototype of a GAN-based Peptide Design Method

Published: 04 September 2019 Publication History

Abstract

Pharmaceutical drug design is a difficult and costly endeavor. Computational drug design has the potential to help save time and money by providing a better starting point for new drugs with an initial computational evaluation completed. We propose a new application of Generative Adversarial Networks (GANs), called GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier) to design new peptides for protein targets. Other GAN based methods for computational drug design can only generate small molecules, not peptides. It also incorporates data such as active atoms, not used in other methods, which allow us to precisely identify where interaction occurs between a protein and ligand. Our method goes farther than comparable methods by generating a peptide and predicting binding affinity. We compare results for a protein of interest, PD-1, using: GANDALF, Pepcomposer, and the FDA approved drugs. We find that our method produces a peptide comparable to the FDA approved drugs and better than that of Pepcomposer. Further work will improve the GANDALF system by deepening the GAN architecture to improve on the binding affinity and 3D fit of the peptides. We are also exploring the uses of transfer learning.

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Cited By

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  • (2024)Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptidesBriefings in Bioinformatics10.1093/bib/bbae44425:5Online publication date: 10-Sep-2024
  • (2023)Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide DesignJournal of Chemical Information and Modeling10.1021/acs.jcim.3c0188164:1(316-326)Online publication date: 22-Dec-2023
  • (2023)PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial NetworkSN Computer Science10.1007/s42979-023-02203-34:5Online publication date: 11-Aug-2023
  • Show More Cited By

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cover image ACM Conferences
BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
September 2019
716 pages
ISBN:9781450366663
DOI:10.1145/3307339
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 September 2019

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Author Tags

  1. computational drug design
  2. deep learning
  3. generative adversarial networks
  4. machine learning

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BCB '19 Paper Acceptance Rate 42 of 157 submissions, 27%;
Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

View all
  • (2024)Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptidesBriefings in Bioinformatics10.1093/bib/bbae44425:5Online publication date: 10-Sep-2024
  • (2023)Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide DesignJournal of Chemical Information and Modeling10.1021/acs.jcim.3c0188164:1(316-326)Online publication date: 22-Dec-2023
  • (2023)PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial NetworkSN Computer Science10.1007/s42979-023-02203-34:5Online publication date: 11-Aug-2023
  • (2022)De Novo Peptide and Protein Design Using Generative Adversarial Networks: An UpdateJournal of Chemical Information and Modeling10.1021/acs.jcim.1c0136162:4(761-774)Online publication date: 7-Feb-2022
  • (2020)Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein DesignMolecules10.3390/molecules2514325025:14(3250)Online publication date: 16-Jul-2020
  • (2020)Novel Generated Peptides for COVID-19 TargetsProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414919(1-1)Online publication date: 21-Sep-2020

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