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All Journal HAYATI Journal of Biosciences Jurnal Silvikultur Tropika (JST) Jurnal Floratek Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter BIOTROPIA - The Southeast Asian Journal of Tropical Biology AL KAUNIYAH BIOTIK: Jurnal Ilmiah Biologi Teknologi dan Kependidikan Journal of Aceh Physics Society Jurnal Penelitian Pendidikan IPA (JPPIPA) Prosiding Seminar Nasional Biologi, Teknologi dan Kependidikan (Biotik) Makara Journal of Science Jurnal Natural Journal of Patchouli and Essential Oil Products Jurnal Ilmiah Mahasiswa Pertanian Heca Journal of Applied Sciences Grimsa Journal of Science Engineering and Technology Biosaintifika: Journal of Biology & Biology Education Biosainsdik: Biologi Sains dan Kependidikan
Essy Harnelly
Biology Department, Faculty Of Mathematics And Natural Sciences, Syiah Kuala University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Heca Journal of Applied Sciences

 1 
Predicting AXL Tyrosine Kinase Inhibitor Potency Using Machine Learning with Interpretable Insights for Cancer Drug Discovery Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Harnelly, Essy; Sari, Irma; Fauzi, Fazlin Mohd; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v3i1.270

Abstract

AXL tyrosine kinase plays a critical role in cancer progression, metastasis, and therapy resistance, making it a promising target for therapeutic intervention. However, traditional drug discovery methods for developing AXL inhibitors are resource-intensive, time-consuming, and often fail to provide detailed insights into molecular determinants of potency. To address this gap, we applied machine learning techniques, including Random Forest, Gradient Boosting, Support Vector Regression, and Decision Tree models, to predict the potency (pIC50) of AXL inhibitors using a dataset of 972 compounds with 550 molecular descriptors. Our results demonstrate that the Random Forest model outperformed others with an R² of 0.703, MAE of 0.553, RMSE of 0.720, and PCC of 0.841, showcasing strong predictive accuracy. SHAP analysis identified critical molecular features, such as RNCG and TopoPSA(NO), as key contributors to inhibitor potency, providing interpretable insights into structure-activity relationships. These findings highlight the potential of machine learning to accelerate the identification and optimization of AXL inhibitors, bridging the gap between computational predictions and rational drug design and paving the way for effective cancer therapeutics.
Co-Authors Adrian Damora Afidh, Razief Perucha Fauzie Agus Sara Akhyar, Fikrul Amirunnas , Amirunnas Annisa Ammalia Kiti Ardhana Yulisma Arif Habibal Umam Ariqah, Nada Azzahra, Syarifah Fathimah Cici Ariska A Dina Firmadiana Fahira, Cut Nathasya Faradhila, Jihan Fauzi, Fazlin Mohd Fauziah . Fenty Ferayanti Gani, Fadli A. Habib, Ahasan Hairul Basri Hawati Hawati HENDRIX INDRA KUSUMA Hermanto, Feri Eko Idroes, Ghifari Maulana Iqbar Iqbar Iqbar Irma Dewiyanti Irma Fitri, Irma Irma Sari ISKANDAR ZULKARNAIN SIREGAR Isnaini, Nadia Itawarnemi, Hilmina Khairan Khairan Kiti, Annisa Ammalia Kusuma, Hendrix KUSUMA, HENDRIX INDRA Lenni Fitri Lydia Septa Desiyana, Lydia Septa M. Satria Fitri Malahayati T. Hanafiah Martunis - Martunis Martunis Maysarah, Hilda Meutia Zahara Mira Humaira Misbullah, Alim Misrahanum Misrahanum MUHAMMAD ADRIYAN FITRA MUHAMMAD BAHI Muhammad Irfan Muhammad Rusdi Muhammad Subianto Munira, Alya Mutia Zahara Nafissa, Naja Nanda Muhammad Razi Nazaruddin Nir Fathiya NITA TAUHIDA NITA TAUHIDA Nur Fadli Nurur Rahmy Oktaviana, Nurul Onny Ulfa Rahayu Prajaputra, Vicky Rahayu, Sri Riska Ramadhaniaty, Mutia Ramlan, Risa Riani Rauzana, Anita RIDHA UL FAHMI Rina Sriwati Rinaldi Idroes Rizki Amelia Rizki, Alia Rizky Amelia Rusita Fitri Saida Rasnovi Saida Rasnovi SAIDA RASNOVI Samingan Samingan Sari, Febia Saudah Saudah SITI MARYAM Siti-Azizah, Mohd Nor Sreeramanan Subramaniam Sri Jumiati, Sri Sri Wati Suryadi Suryadi Syaharani, Cut Puspita Salsabila Syahraini, Aigia Syaifullah Muhammad Teuku Rizky Noviandy Ulfa hansri Ar-Rasyid Widya Sari Wira Dharma Wisnu Ananta Kusuma Yekki Yasmin Yudi Haditiar YUNITA Yunita Yunita Yurinda Yurinda ZAINAL ABIDIN MUCHLISIN ZAIRIN THOMY Zairin Thomy Zairin Thomy Zairin Thomy Zairin Thomy Zairin Thomy Zairin Thomy Zairin Thomy Zairin Thomy Zakaria, Rahmad Zulfan Zulkarnain Zulkarnain Zulwanis, Zulwanis Zumaidar Zumaidar Zumaidar