Development and Application of Structure Assisted Atom Based de Novo Molecular Design (SAAD) Approach using matlab




denovo, matlab, drug design, atom-based


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.


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.



How to Cite

Al-Qattan, M., & Mordi, M. N. (2023). Development and Application of Structure Assisted Atom Based de Novo Molecular Design (SAAD) Approach using matlab. Pharmacy and Applied Health Sciences, 2(1), 9–22.



Research Article