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Farrah Fahdhienie
Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Aceh, Banda Aceh
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Journal : Narra X

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Random forest-based QSAR modeling for predicting the potency of neprilysin inhibitors using Mordred molecular descriptors Albar, Nizam; Rampengan, Derren DCH.; Azhari, Saiful; Mahmudi, Mahmudi; Fahdhienie, Farrah; Susilawati, Anggi; Habiburrahman, Muhammad
Narra X Vol. 4 No. 1 (2026): April 2026 (In Press)
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v4i1.242

Abstract

Neprilysin (NEP) is a zinc-dependent metallopeptidase, considered a key therapeutic target in heart failure management. Efficient identification of potent NEP inhibitors remains a challenge in drug discovery. The aim of this study was to develop a quantitative structure–activity relationship (QSAR) model using 2D Mordred molecular descriptors and Random Forest algorithms to predict the inhibitory potency (pIC50) of drug candidates. A curated dataset of compounds with experimentally determined IC₅₀ values (in nM) against NEP was preprocessed and converted to pIC50. Mordred was used to calculate 2D molecular descriptors, and descriptors with missing values were excluded. The dataset was split into training, internal validation, and external test sets. A Random Forest regression model was trained using 500 estimators, and model performance was evaluated using R2, root mean square error (RMSE), mean absolute error (MAE), and concordance correlation coefficient (CCC), while a binary classification model was also constructed. Feature importance, residual analysis, and chemical space visualization were conducted to assess model interpretability and reliability. The regression model demonstrated moderate to strong predictive performance, with R2 of 0.286, RMSE of 0.949, MAE of 0.723, and CCC of 0.532 in the internal validation. External validation showed improved generalization, with R2=0.659, RMSE=0.858, MAE=0.630, and CCC=0.763. Binary classification revealed an accuracy of 0.953, precision of 1.000, recall of 0.943, and an F1-score of 0.971, indicating strong discriminative ability in classifying inhibitory versus non-inhibitory compounds. Top contributing descriptors included ATSC2p (feature importance=0.0505), GATS2p (0.0408), and SaasC (0.0317). Principal component analysis (PCA) and Williams plots confirmed that test compounds lie within the model’s applicability domain, with no major outliers in leverage or residual distribution. The developed Random Forest-based QSAR model demonstrates strong predictive power and interpretability for identifying NEP inhibitors. This study provides a valuable tool for virtual screening and highlights the relevance of 2D structural features in governing NEP inhibitory activity. It is the first dedicated QSAR analysis of neprilysin inhibition using Mordred descriptors with rigorous internal and external validation.
Co-Authors Abdi, Naufal Abdullah, Asnawi Abidin, Taufik F. Achmad, Asmawati Afriliansyah Sihotang Afriliansyah, Afriliansyah Agustin, Rahmanda Amelia Agustina Agustina Agustina Agustina Agustina Ainul Fadla Akbar, Azrial Akbar, Fahrisal Akhmalnihar, Ulfa Albar, Nizam Amalia, Anasya Amara Rizal, Audia Amin, Fauzi Ali Andrean, Heppy Maulizar Andria, Dedi Anggi Susilawati Anggun Paramita Djati Arafani, Vilda Arbi, Anwar ARBIE, ANWAR Arifin, Vera Nazhira Arifin, Vera Nazira Arifin, Vera Nazirah Ariscasari, Putri Arlianti, Nopa Asnawi Abdullah Atira, Dhia Ayu Vinezia Situmeang Azhari, Saiful Azwar, Eddy Baharuddin , Dharina Baharuddin, Dharina Basri Aramico, Basri Bella, Natha Damayanti, Elok Darmawan, Aldi Darwis, Aryandi Dede Dessy Listyana Depa Raihan Desi Ari Apsari Devi Zulfika Dharina Baharuddin Diana, Mutia Eddy Azwar Elvira Nova Riany Erma Wina Erwiyanti, Sherly Nurfajri Fadhlullah Fadhlullah Fadhlullah, Fadhlullah Fahmi Ichwansyah Faisal Abdurahman fajri, izzarul fahmi Fathiariani, Liza febrianti, heni Febriyanti, Heni Fitri, Raihanil Hafnidar, Hafnidar Hamisah, Irma Handayani, Yusra Haris Ferdinal Setiawan Hasnaini, Cika Hayatunnufus Hayatunnufus Hermansyah Hermansyah Humaira, Adintya Ibrahim, Aisha Humaira Ichwamsyah, Fahmi Ihsan, Ahadun Ipah Riani Iqhrammullah, Muhammad Irma Hamisah Irma Linda Jabari, Jabari Julamnur, Ratifa Kamalurrijal, Kamalurrijal Kartini Kartini Kausar, Muhammad Anis AL Khairunnisa LB, ROSMAINI Lheena, Cut Putri Zaila M. Marthoenis Mahmudi Mahmudi Maidar Maidar Maidar, Maidar Maidar, Maidar Mainimar Mainimar Mairani, Tiara MAQFIRAH, MONA HILOKA Maqfirah, Rauzatul Marhamah Marhamah Marzuki Marzuki Maudi, Viranika Maulana, Aiga Maulinasari, Maulinasari Melania Hidayat Meutia Zahara Mhd. Nauval Ridha Mudatsir Mudatsir Muhammad Ghifari Karsa Muhammad Habiburrahman, Muhammad Muhammad Iqbal Muharani, Muharani Mulia, Syarifah Sulfira Mushaddiq, Muhammad Mutia, Sunnia Nahrisah, Putri Naila Sabila Nana Wildana Natha Bella Nauval, M. Dharma Nazri Fajrianda Nengsi, Poppy Sri Ningsih, Rima Wirayudha Nisak, Khairun Nisrina Nisrina Nova Septiana Puteri Nurjannah, Nurjannah Nurmaili Nurmaili Phossy Vionica Ramadhana Pratiwi, Agil Putri Balqis, Putri Putri Nur Ramadhani Putri Riza Yana Radhiah Zakaria Rahayu, Rauza Lestari Rahma Wati Rahmadhani, Syahri Rahmadhaniah Rahmadhaniah Rahmah, Fitri Mauliza Raiyan, Deka Ramadhana, Phossy Vionica Ramadhani, Putri Nur Ramadhani, Tiara Rampengan, Derren DCH. Rasmawati, Rasmawati Rawanita, Misdar Regina, Farin Rezeki, Tamara Riany, Elvira Nova Ricinta, Putroe Aliya Rihadah, Sofaa Risa Hajjatul Ula Riska Riska Rosaria Indah Saiful Kamal sainah, Nur Santika, Deva saputra, T. Yudha Sari Wartini Sari, Herdinda Mutiara Savitri, Hidayati Septiani, Riza Siska Riani Soleha, Rifa Sonia Bella Sanjaya Sugisni, Susi Syahriazi Tahara Dilla Santi Tahara Dilla Santi Tapa, Rd Baresi Sananda ULUL AZMI Utari, Nadia Vera Nazhira Arifin Vera Nazhira Arifin Vera Nazhira Arifin Vera Nazhira Arifin Vera Nazhira Arivin Waliyyuddin, Rajwa Wardiati, Wardiati Wartini, Sari Wiwit Aditama Yesi Yuliana Yesika Amananda Yusafrita, Yusafrita Zahara, Meutia Zakaria , Radhiah Zakaria, Radhiah Zuhriyyah, Azqia