Author(s)

Dawei Feng ¹

Affiliation(s)

¹ School of Pharmacy, Yantai University, Yantai, 264005, China

Corresponding Author

Dawei Feng

ABSTRACT

Designing molecules with certain physicochemical features can promote the discovery and optimization of lead compounds. However, most molecular generation models optimize only one physicochemical property, which is not sufficient to determine the availability of a drug. This is because the availability of a drug substance molecule depends on the combined effect of many physicochemical properties. In this study, the pareto method was conducted to optimize the compounds for multi-target molecular characteristics in close approximation to those of the reference compound. In addition, we similarly used the random SMILES method involving amplification and diversification of molecules. Finally, we further examined the generation ability of the model and also analyzed the probability distribution of the physicochemical attributes and molecular structure of the created compounds. We expect that the model could develop additional molecules for exploring a bigger chemical space for medicinal chemists.

KEYWORDS

Molecular generator, Multi-objective optimization, Pareto algorithm

CITE THIS PAPER

Dawei Feng, Molecular Generator for Multi-objective Optimization Based on the Pareto Algorithm. MEDS Clinical Medicine (2023) Vol. 4: 55-66. DOI: http://dx.doi.org/10.23977/medsc.2023.040809.

REFERENCES

[1] Walters, W.P. and R. Barzilay, Applications of Deep Learning in Molecule Generation and Molecular Property Prediction. Acc Chem Res, 2021. 54(2): p. 263-270.
[2] Tong, X., et al., Generative Models for De Novo Drug Design. J Med Chem, 2021. 64(19): p. 14011-14027.
[3] D'Souza, S., P. Kv, and S. Balaji, Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery? Expert Opin Drug Discov, 2022. 17(10): p. 1071-1079.
[4] Kong, W., et al., Application of SMILES-based molecular generative model in new drug design. Front Pharmacol, 2022. 13: p. 1046524.
[5] Bai, X.Y. and Y.X. Yin, Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development. Journal of Cheminformatics, 2021. 13(1).
[6] Joo, S., et al., Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder. Acs Omega, 2020. 5(30): p. 18642-18650.
[7] Lim, J., et al., Molecular generative model based on conditional variational autoencoder for de novo molecular design. J Cheminform, 2018. 10(1): p. 31.
[8] Cheng, Y., et al., Molecular design in drug discovery: a comprehensive review of deep generative models. Briefings in Bioinformatics, 2021. 22(6).
[9] Lamanna, G., et al., GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design. J Chem Inf Model, 2023. 63(16): p. 5107-5119.
[10] Xu, T.X., et al., A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information. Molecular Informatics, 2022. 41(12).
[11] Choi, J., S. Seo, and S. Park, COMA: efficient structure-constrained molecular generation using contractive and margin losses. Journal of Cheminformatics, 2023. 15(1).
[12] Arús-Pous, J., et al., Randomized SMILES strings improve the quality of molecular generative models. J Cheminform, 2019. 11(1): p. 71.
[13] Saikia, S. and M. Bordoloi, Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets, 2019. 20(5): p. 501-521.
[14] Shivanyuk, A.N., et al., Enamine real database: Making chemical diversity real. Chimica Oggi, 2007. 25(6): p. 58-59.
[15] RDKit: Open-Source Cheminformatics. http://www.rdkit.org.
[16] Kim, K.H. and C.W.M. Roberts, Targeting EZH2 in cancer. Nature Medicine, 2016. 22(2): p. 128-134.
[17] Zhou, B., et al., Discovery of IHMT-EZH2-115 as a Potent and Selective Enhancer of Zeste Homolog 2 (EZH2) Inhibitor for the Treatment of B-Cell Lymphomas. Journal of Medicinal Chemistry, 2021. 64(20): p. 15170-15188.
[18] Yap, T.A., et al., Phase I Study of the Novel Enhancer of Zeste Homolog 2 (EZH2) Inhibitor GSK2816126 in Patients with Advanced Hematologic and Solid Tumors. Clinical Cancer Research, 2019. 25(24): p. 7331-7339.
[19] Berenger, F., O. Vu, and J. Meiler, Consensus queries in ligand-based virtual screening experiments. Journal of Cheminformatics, 2017. 9.
[20] Liu, X., et al., DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology. J Cheminform, 2021. 13(1): p. 85.
[21] Lotfollahi, M., et al., Conditional out-of-distribution generation for unpaired data using transfer VAE. Bioinformatics, 2020. 36(Suppl_2): p. i610-i617.
[22] Bjerrum, E.J. and B. Sattarov, Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders. Biomolecules, 2018. 8(4).
[23] Jørgensen, P.B., M.N. Schmidt, and O. Winther, Deep Generative Models for Molecular Science. Mol Inform, 2018. 37(1-2).
[24] Kim, K.S. and Y.S. Choi, HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks. Sensors (Basel), 2021. 21(12).
[25] Arús-Pous, J., et al., SMILES-based deep generative scaffold decorator for de-novo drug design. J Cheminform, 2020. 12(1): p. 38.
[26] Benhenda, M., ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? Arxiv, 2017.
[27] Schöning-Stierand, K., et al., ProteinsPlus: a comprehensive collection of web-based molecular modeling tools. Nucleic Acids Research, 2022. 50(W1): p. W611-W615.
[28] Schöning-Stierand, K., et al., ProteinsPlus: interactive analysis of protein–ligand binding interfaces. Nucleic Acids Research, 2020. 48(W1): p. W48-W53.
[29] Fährrolfes, R., et al., ProteinsPlus: a web portal for structure analysis of macromolecules. Nucleic Acids Research, 2017. 45(W1): p. W337-W343.

Sponsors, Associates, and Links

---

• Journal of Neurobiology and Genetics
  
  
• Medical Imaging and Nuclear Medicine
  
  
• Bacterial Genetics and Ecology
  
  
• Transactions on Cancer
  
  
• Journal of Biophysics and Ecology
  
  
• Journal of Animal Science and Veterinary
  
  
• Academic Journal of Biochemistry and Molecular Biology
  
  
• Transactions on Cell and Developmental Biology
  
  
• Rehabilitation Engineering & Assistive Technology
  
  
• Orthopaedics and Sports Medicine
  
  
• Hematology and Stem Cell
  
  
• Journal of Intelligent Informatics and Biomedical Engineering
  
  
• MEDS Basic Medicine
  
  
• MEDS Stomatology
  
  
• MEDS Public Health and Preventive Medicine
  
  
• MEDS Chinese Medicine
  
  
• Journal of Enzyme Engineering
  
  
• Advances in Industrial Pharmacy and Pharmaceutical Sciences
  
  
• Bacteriology and Microbiology
  
  
• Advances in Physiology and Pathophysiology
  
  
• Journal of Vision and Ophthalmology
  
  
• Frontiers of Obstetrics and Gynecology
  
  
• Digestive Disease and Diabetes
  
  
• Advances in Immunology and Vaccines
  
  
• Nanomedicine and Drug Delivery
  
  
• Cardiology and Vascular System
  
  
• Pediatrics and Child Health
  
  
• Journal of Reproductive Medicine and Contraception
  
  
• Journal of Respiratory and Lung Disease
  
  
• Journal of Bioinformatics and Biomedicine