Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Molecule Generation of Drugs Using VAE

Authors
K. B. Anusha1, *, Modalavalasa Divya1, K. Madhuri Pravallikha Rani2, B. Satvika2, P. Tarun2, G. Vaishnavi2, S. Linga Raju2
1Assistant Professor, Department of Computer Science & Engineering, Aditya Institute of Technology and Management, Tekkali, 532201, India
2B. Tech. Students, Department of Computer Science & Engineering, Aditya Institute of Technology and Management, Tekkali, 532201, India
*Corresponding author. Email: anushakb91@gmail.com
Corresponding Author
K. B. Anusha
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_17How to use a DOI?
Keywords
VAE,SMILES,KL Divergence loss; Categorical cross entropy
Abstract

The field of drug discovery and development has witnessed a transformative change during the previous few years with application in artificial intelligence and ML techniques. Among these, Variational Autoencoders (VAEs) have emerged promising instrument for the generative designing of drug molecules. In relation to drugs discovery, VAEs have been employed to encode and decode chemical structures, facilitating the generation of drug molecules. This is achieved through the encoding of chemical configurations into an uninterrupted latent space, where the generative capacity of the model can be harnessed to create diverse and potentially pharmacologically relevant compounds. Key components of this approach include the representation of molecules as graphs or SMILES (Simplified Molecular Input Line Entry System) strings, In development of specialized loss functions to optimize characteristics of molecules, and the investigating the latent space to produce molecules with desired characteristics. Hence, In this project, we build compounds for drug discovery using a variational autoencoder.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_17How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. B. Anusha
AU  - Modalavalasa Divya
AU  - K. Madhuri Pravallikha Rani
AU  - B. Satvika
AU  - P. Tarun
AU  - G. Vaishnavi
AU  - S. Linga Raju
PY  - 2024
DA  - 2024/07/30
TI  - Molecule Generation of Drugs Using VAE
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 170
EP  - 179
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_17
DO  - 10.2991/978-94-6463-471-6_17
ID  - Anusha2024
ER  -