Citation: Amiroch, S.; Irawan, M.I.; Mukhlash, I.; Al Faroby, M.H.Z.; Nidom, C.A. Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins. Symmetry 2022, 14, 1114. https:// doi.org/10.3390/sym14061114 Academic Editor: Leyi Wei Received: 20 April 2022 Accepted: 23 May 2022 Published: 28 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). symmetry S S Article Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins Siti Amiroch 1,2 , Mohammad Isa Irawan 1, * , Imam Mukhlash 1 , Mohammad Hamim Zajuli Al Faroby 3 and Chairul Anwar Nidom 4,5 1 Department of Mathematics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia; siti.amiroch@unisda.ac.id (S.A.); imamm@matematika.its.ac.id (I.M.) 2 Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Islam Darul ‘Ulum, Lamongan 62253, Indonesia 3 Department of Data Science, Institut Teknologi Telkom Surabaya, Surabaya 60231, Indonesia; alfaroby@ittelkom-sby.ac.id 4 Coronavirus and Vaccine Formulation Research Group, Professor Nidom Foundation, Surabaya 60115, Indonesia; nidomca@pnfinstitute.org 5 Faculty of Veterinary Medicine, Airlangga University, Surabaya 60115, Indonesia * Correspondence: mii@its.ac.id; Tel.: +62-81233555479; Fax: +62-031-5996506 Abstract: Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings. Keywords: machine learning; significant compounds; avian influenza A/H9N2; antivirus 1. Introduction Avian influenza A/H9N2 is a novel bird flu virus first detected in December 2016 in South Sulawesi, Indonesia [1]. Reverse transcription polymerase chain reaction results suggested that the virus was present in chicken farms from December 2016 to May 2017 [2]. Phylogenetic analysis using the Felsenstein model neighbor-joining algorithm simulated in MATLAB ® identified 13 clusters of H9N2 that have spread throughout Indonesia [3]. H9N2 is classified as a low pathogenic avian influenza virus or a “bird flu” and was not initially thought to infect humans [4]. Nonetheless, H9N2 replicates in the respiratory and reproductive tracts of chicken hosts, thereby reducing egg production [5] by as much as 80% [1]. Additionally, recent research has shown that the H9N2 virus can also reproduce in mammalian lungs and reproductive organs (including the ovaries, fallopian tubes, and uterus), even in humans [6]. These findings can help us understand how potential pathogens such as the H9N2 virus may threaten human health. Symmetry 2022, 14, 1114. https://doi.org/10.3390/sym14061114 https://www.mdpi.com/journal/symmetry