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Enhancing Pharmaceutics With AI Through Predictive Modeling of Crystal Structures and Atom Properties for Improved Solubility and Bioavailability
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Author(s): Sekar Kidambi Raju (School of Computing, SASTRA University, Thanjavur, India), Subhash Kannan (K. Ramakrishnan College of Engineering, Samayapuram, India), Ganesh Karthikeyan Varadarajan (School of Computing, SASTRA University, Thanjavur, India), Raj Anand Sundaramoorthy (School of Computing, SASTRA University, Thanjavur, India), Marwa M. Eid (Jadara University Research Center, Jadara University, Jordan)and El-Sayed M. El-Kenawy (Applied Science Research Center, Applied Science Private University, Amman, Jordan)
Copyright: 2026
Pages: 34
Source title:
Chalcogenide-Based Materials for Optoelectronics, Energy, and Sustainability
Source Author(s)/Editor(s): Karthik Kannan (National Chung Cheng University, Taiwan & Karpagam Academy of Higher Education, India)and Vinaya Tari (Universitas Airlangga, Indonesia)
DOI: 10.4018/979-8-3373-3962-7.ch017
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Abstract
The research study explores how sophisticated machine learning algorithms, i.e., our suggested model SENET, coupled with SVM and ADAM, could enhance drug solubility and bioavailability through the prediction of crystallographic structures and atom property classification. The current research aims to revolutionize pharmaceutical drug development processes by embracing AI-based preclinical Testing. We investigate the sophisticated field of crystallographic structures and dissect atomistic data in depth to reveal determining factors influencing crystal forms. The aim of the research is to enhance drug solubility and bioavailability by conducting this study, which will significantly transform pharmaceutical practice. The study shows that sophisticated machine learning methods can potentially forecast drug behavior in complicated situations. This study discloses the potential for groundbreaking drug discovery by speeding up the progress in pharmaceutical science, reducing the development cost, and optimizing patient benefit through enhanced drug delivery and action.
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