Artificial intelligence in solubility prediction for poorly soluble drugs.

Registration ID: IJNRD_310428

Published ID: IJNRD2511228

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Surve Avijita Jitendra, Suryawanshi Jagruti, Deepika Patil, Shinde Ajinkya, Shirude Darshan

Solubility prediction, Artificial intelligence, Machine Larning, Deep Learning, Poorly soluble drugs, Quality by Design, Drug Development Bioavailability

Solubility is one of the most important physicochemical properties in pharmaceutical development. It refers to the ability of a substance—such as a drug compound—to dissolve in a given solvent, typically water for oral medications. For a drug to be effective, it must first dissolve in the body’s fluids so that it can be absorbed into the bloodstream and reach its site of action. However, many newly discovered drug molecules are poorly soluble in water. In fact, more than 40% of currently marketed drugs and up to 90% of molecules in development face solubility-related challenges. Poor aqueous solubility often leads to low bioavailability, inconsistent drug absorption, and therapeutic failure, which in turn increases the time, cost, and risk of drug development. To overcome these challenges, solubility enhancement has become a major focus in pharmaceutical formulation. Several traditional approaches have been employed, including salt formation, particle size reduction, solid dispersions, use of surfactants, and complexation. However, these methods can be time-consuming, expensive, and sometimes ineffective—especially during early-stage drug discovery when thousands of candidate molecules must be screened quickly. This is where Artificial Intelligence (AI) and Machine Learning (ML) are proving to be game-changers. These computational methods can predict the solubility of a drug molecule based on its chemical structure, saving time and reducing the need for extensive lab testing. ML models are trained on large datasets containing known drug solubility values and learn to recognize patterns that influence solubility, such as molecular size, polarity, and functional groups. Algorithms like Random Forests, Neural Networks, and Support Vector Machines have shown promising results in predicting solubility with high accuracy. For example, Lovrić et al. (2021) reported that Random Forest models achieved an R² of 0.83 and RMSE of 0.75–1.05 log units when predicting intrinsic solubility from molecular descriptors [1]. In addition to these traditional approaches, there is growing interest in using green and sustainable technologies to enhance solubility. One such method is supercritical carbon dioxide (SCCO₂), a non-toxic, environmentally friendly solvent used to improve solubility and create advanced formulations such as drug nanoparticles. AI models are now being applied to predict how different drugs behave in SCCO₂ systems. A recent study by Jamshidi et al. (2024) used machine learning models to estimate the solubility of ketoprofen in SCCO₂, achieving an R² of over 0.94, which indicates strong predictive performance [2]. AI doesn’t just improve prediction—it also supports continuous manufacturing, which is a modern approach to drug production that is faster, more efficient, and less wasteful compared to traditional batch methods. AI models can help control key process parameters such as pressure, temperature, and mixing rates in real time, making them ideal tools for future-ready pharmaceutical development. In summary, poor solubility is a major barrier in drug development, but with the help of AI and ML, researchers can now predict solubility more accurately, screen molecules faster, and design better formulations using both conventional and green technologies. This review brings together key findings from both traditional ML models and recent green-solvent approaches, showing how AI is reshaping the way we handle poorly soluble drugs—making the process faster, more sustainable, and more reliable.

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Surve Avijita Jitendra, Suryawanshi Jagruti, Deepika Patil, Shinde Ajinkya, Shirude Darshan, & Sonawane Darshan (November-2025). Artificial intelligence in solubility prediction for poorly soluble drugs. . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(11), c285-c296. https://ijnrd.org/papers/IJNRD2511228.pdf

Volume 10 Issue 11, November-2025

Pages : c285-c296

Paper Reg. ID: IJNRD_310428

Published Paper Id: IJNRD2511228

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