Development and Application of Structure Assisted Atom Based de Novo Molecular Design (SAAD) Approach using matlab
DOI:
https://doi.org/10.59480/phahs.v1i2.15Keywords:
denovo, matlab, drug design, atom-basedAbstract
In the context of drug design and development, phytochemical and synthetic libraries are starting places for searching leads. However, knowing crystal structure of receptor may help scratching molecules that complements available interaction sites using de novo molecular design approaches. This research describes the implementation of genetic algorithm optimization technique for construction of molecular scaffold composed from sp2 carbon, oxygen and nitrogen atoms. Using Matlab, functions for molecule construction, crossover, mutation and fitness measurement were individually programmed. Fitness function was derived from AutoDock3.05 function which calculates molecular interaction energy with protein using grid map. The approach was successfully applied to design glutathione-S-transferase inhibitory molecule at sub-site of the binding pocket that is originally occupied by g-glutamyl and cysteinyl moieties of glutathione substrate. Starting from scratches, the approach was able to design molecular scaffolds complementarily filling the pocket.
References
ARMSTRONG, R. N. 1997. Structure, catalytic mechanism, and evolution of the glutathione transferases. Chem Res Toxicol, 10, 2-18.
CHU, Y. & HE, X. 2019. Molegear: A java-based platform for evolutionary de novo molecular design. Molecules, 24, 1444.
DEVI, R. V., SATHYA, S. S. & COUMAR, M. S. 2014. Gamol: Genetic algorithm based de novo molecule generator.
HERRING, R. H. & EDEN, M. R. 2015. Evolutionary algorithm for de novo molecular design with multi-dimensional constraints. Computers & Chemical Engineering, 83, 267-277.
KUSNER, M. J., PAIGE, B. & HERNÁNDEZ-LOBATO, J. M. 2017. Grammar variational autoencoder. In: DOINA, P. & YEE WHYE, T. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research: PMLR.
LEGUY, J., CAUCHY, T., GLAVATSKIKH, M., DUVAL, B. & DA MOTA, B. 2020. Evomol: A flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. Journal of Cheminformatics, 12, 55.
MEYERS, J., FABIAN, B. & BROWN, N. 2021. De novo molecular design and generative models. Drug Discovery Today.
MORRIS, G. M., HUEY, R. & OLSON, A. J. 2008. Using autodock for ligand-receptor docking. Current Protocols in Bioinformatics, 24, 8.14.1-8.14.40.
OLIVECRONA, M., BLASCHKE, T., ENGKVIST, O. & CHEN, H. 2017. Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics, 9, 48.
PEGG, S. C., HARESCO, J. J. & KUNTZ, I. D. 2001. A genetic algorithm for structure-based de novo design. Journal of computer-aided molecular design, 15, 911-33.
ROTSTEIN, S. & MURCKO, M. 1993. Genstar: A method for de novo drug design. J. Comput. Aided Mol. Des., 7, 23-43.
SCHNEIDER, G. 2013. De novo molecular design, John Wiley & Sons.
SEGLER, M. H. S., KOGEJ, T., TYRCHAN, C. & WALLER, M. P. 2018. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Science, 4, 120-131.
SPIEGEL, J. O. & DURRANT, J. D. 2020. Autogrow4: An open-source genetic algorithm for de novo drug design and lead optimization. Journal of Cheminformatics, 12, 25.
YOSHIKAWA, N., TERAYAMA, K., SUMITA, M., HOMMA, T., OONO, K. & TSUDA, K. 2018. Population-based de novo molecule generation, using grammatical evolution. Chemistry Letters, 47, 1431-1434.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 mohammed alqattan, Mohd Nizam Mordi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All PHAHS Journal articles are published under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) which allows authors retain copyright and others may not use the material for commercial purposes. A commercial use is one primarily intended for commercial advantage or monetary compensation. If others remix, transform, or build upon the material, they may not distribute the modified material.
Copyright on any research article published by the PHAHS Open Access journal is retained by the author(s). Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Use of the article in whole or part in any medium requires attribution suitable in form and content as follows: [Title of Article/Author/Journal Title and Volume/Issue. Copyright (c) [year] [copyright owner as specified in the Journal]. Links to the final article on PHAHS website are encouraged where applicable.
The CC BY-NC-ND 4.0 Creative Commons Attribution License does not affect the moral rights of authors, including without limitation the right not to have their work subjected to derogatory treatment. It also does not affect any other rights held by authors or third parties in the article, including trademark or patent rights, or the rights of privacy and publicity. Use of the article must not assert or imply, whether implicitly or explicitly, any connection with, endorsement or sponsorship of such use by the author, publisher or any other party associated with the article.