Citation :

Sandhi Kranthi Reddy, S V G Reddy, "A Novel Multifaceted and Multitargeted Approach to Predict the Efficacy of New SMILE for NSCLC using Graph Attention Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 12, pp. 244-265, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I12P120

Abstract

NSCLC - Non-Small Cell Lung Cancer, which holds almost 85% cases of lung cancer, is one of the deadliest diseases worldwide and a leading cause of death related to cancer. Types of NSCLC are Adenocarcinoma, Large cell carcinoma, and Squamous cell carcinoma. Among these, adenocarcinomas account for 40%-50% of NSCLC cases that occur more among youngsters, non-smokers, and East Asians and are often diagnosed at advanced stages, which remains a challenge for their better treatment. NSCLC occurs due to a wide range of targetable alterations, among which EGFR, ALK, KRAS, and PDGFR account for numerous cases. The emergence of artificial intelligence has accelerated the early detection of NSCLC using various machine learning and deep learning models based on numerical or image datasets, but there is a huge requirement to shift the focus to identifying a novel drug that could work effectively at an early or advanced stage. Existing drugs may become resistant after some time, and there will always be a huge requirement to develop a new drug, which perhaps requires a lengthy amount of time and more cost using traditional approaches, and it is even a risky process since 97% of drug discoveries fail. Hence, it is necessary to build and use machine learning or deep learning models to estimate the ability of a new drug as a part of lead identification before moving to further processing. To address this, a multifaceted and multitargeted approach using Graph Attention Networks has been proposed, designing a model that is trained using 15 FDA-approved drugs and a vast library of 1.048 million drug molecules to predict the efficiency of a new drug, which achieved 89% accuracy. In the drug discovery process, this highlights the potential of deep learning, which provides enhanced, cost-effective, and efficient means to identify novel drugs for the treatment of NSCLC.

Keywords

NSCLC - Non-Small Cell Lung Cancer, which holds almost 85% cases of lung cancer, is one of the deadliest diseases worldwide and a leading cause of death related to cancer. Types of NSCLC are Adenocarcinoma, Large cell carcinoma, and Squamous cell carcinoma. Among these, adenocarcinomas account for 40%-50% of NSCLC cases that occur more among youngsters, non-smokers, and East Asians and are often diagnosed at advanced stages, which remains a challenge for their better treatment. NSCLC occurs due to a wide range of targetable alterations, among which EGFR, ALK, KRAS, and PDGFR account for numerous cases. The emergence of artificial intelligence has accelerated the early detection of NSCLC using various machine learning and deep learning models based on numerical or image datasets, but there is a huge requirement to shift the focus to identifying a novel drug that could work effectively at an early or advanced stage. Existing drugs may become resistant after some time, and there will always be a huge requirement to develop a new drug, which perhaps requires a lengthy amount of time and more cost using traditional approaches, and it is even a risky process since 97% of drug discoveries fail. Hence, it is necessary to build and use machine learning or deep learning models to estimate the ability of a new drug as a part of lead identification before moving to further processing. To address this, a multifaceted and multitargeted approach using Graph Attention Networks has been proposed, designing a model that is trained using 15 FDA-approved drugs and a vast library of 1.048 million drug molecules to predict the efficiency of a new drug, which achieved 89% accuracy. In the drug discovery process, this highlights the potential of deep learning, which provides enhanced, cost-effective, and efficient means to identify novel drugs for the treatment of NSCLC.

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