Integrative Machine learning for Drugs Side Effect Prediction
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Integrative Machine learning for Drugs Side Effect Prediction
Authors:
Laka Sridar dept. AIDS KL University Vijayawada,
Shyam Raj dept. AIDS KL University
Vijayawada, India 2100080056ai.ds@gmail.com
Vujjuru Surya Charan Teja
dept. AIDS KL University
Vijayawada, India charanteja.sai.2003@gmail.com
Abstract—Predicting and understanding the potential side effects of pharmaceutical drugs is a formidable challenge in the realm of medical science. The conventional process of assessing drug safety, primarily reliant on manual clinical testing [3] and post-market surveillance [4], [5], is not only arduous but also time-consuming, making it an impediment to rapid drug development and patient well-being. This paper introduces an innovative approach that harnesses the power of advanced machine learning techniques to address this challenge [6], [7], [8], [10], [9].
Our research delves into the intricate web of drug interactions within the human body and the complex factors contributing to adverse reactions. It is a realm where the exact mechanisms that trigger side effects often remain elusive, and the prevalence of rare and severe reactions complicates the task. Our approach encompasses the integration of diverse data sources, including drug characteristics, generic names, molecular structures (SMILES) [11], [12], and drug category [13]. Thereby providing a comprehensive understanding of the complex relationships between these factors and adverse reactions, unifying these disparate pieces of information, we aim to unveil hidden patterns and relationships that can significantly enhance the accuracy of drug side effect predictions.
In this study, we employ the Random Forest classification model, known for its robustness and interpretability, to make predictions that not only incorporate the broad spectrum of drug-related factors [14] but also ensure the inclusion of rare and severe side effects in the assessment [15]. To address the challenge of handling non-numerical data, we employ methodologies to convert features such as SMILES structures, Drug generic names, and others into meaningful numerical descriptors, enabling their seamless integration into the predictive model.
Our research does not only seek to enhance drug safety evaluation but also endeavors to bridge the chasm between the unpredictability of drug side effects and the need for a more efficient and informed drug development process. With the potential to revolutionize drug safety practices, this research has far-reaching implications in the domain of patient care and drug industry decision-making, promising a safer and more efficient future for the pharmaceutical research industry.
Index Terms—SMILES (Simplified Molecular Input Line En- try System), Indication or Drug Target Diseases, Drug Side Effect, Random Forest, decision tree.
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