A Novel Approach for Evaluating Compound Potential in Lead Identification of Drug Discovery using Machine Learning

DOI

10.2991/978-94-6463-858-5_108How to use a DOI?

Keywords

Drug Discovery; Lead Identification; Molecular Finger Prints; Similarity Metrics; Graph Neural Networks

Abstract

Drug discovery is a challenging, expensive, and time-intensive process involving several critical stages to identify new drug for a particular disease. Among these stages, Lead Identification and Lead Optimization are especially vital. Due to the complexity and resource requirements of the drug development process, novel strategies are required to quickly and effectively determine the potential of compounds from extensive chemical libraries. This paper proposed a novel approach i.e., Advanced Integrated Machine Learning Compound Evaluation Framework (AIMLCEF) designed to evaluate compound potential and streamline the identification of promising drug candidates. The proposed model integrates advanced computational techniques, including molecular fingerprints, similarity metrics, and graph neural networks, to evaluate compound potential. By leveraging extensive data and advanced computational techniques, the model improves both the accuracy and speed of lead identification. This novel approach addresses the common challenges of traditional screening processes and facilitates more effective prioritization of high-potential compounds. By leveraging AIMLCEF, pharmaceutical companies can significantly expedite the identification of high-potential lead compounds from extensive chemical libraries, reducing both time and financial expenditures while enhancing the overall efficiency of drug discovery.

Copyright

© 2025 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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TY  - CONF
AU  - Sandhi Kranthi Reddy
AU  - S. V. G. Reddy
PY  - 2025
DA  - 2025/11/04
TI  - A Novel Approach for Evaluating Compound Potential in Lead Identification of Drug Discovery using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1293
EP  - 1308
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_108
DO  - 10.2991/978-94-6463-858-5_108
ID  - Reddy2025
ER  -