TY - GEN
T1 - Model QSAR Classification Using Conv1D-LSTM of Dipeptidyl Peptidase-4 Inhibitors
AU - Ulfa, Adawiyah
AU - Bustamam, Alhadi
AU - Yanuar, Arry
AU - Amalia, Rizka
AU - Anki, Prasnurzaki
N1 - Funding Information:
Publication of this supplement was partially funded by PUTI Q1 2020 Research Grant (Thesis Indexed International Publication Competitive Grant) from DRPM Universitas Indonesia with contract number NKB-1381/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/28
Y1 - 2021/4/28
N2 - In recent years, various focusing on Dipeptidyl Peptidase-4 inhibitors drugs discovery to achieve better treatments for type II Diabetes Mellitus. As such, new medical research on new DPP-4 inhibitors with minimal effects is still crucial. One of the drug designs based on in silico is a virtual screening-based ligand (LBVS). The LBVS method used in this research is Quantitative structure-activity relation (QSAR). The QSAR model is a fast and cost-effective alternative for experimental measurement in drug discovery. Deep learning has also been successful and is now widely used in drug discovery. In this study, we propose a combination of two deep learning approaches, namely the Conv1D-LSTM model as a renewable method for predicting the classification of Dipeptidyl Peptidase-4 inhibitors. This model includes the Conv1D model as a data encoding stage and LSTM as a model for the classification of compounds in Dipeptidyl Peptidase-4 inhibitors. We use 2604 molecular structures of DPP-4 inhibitors with 1443 active compounds and 1161 inactive compounds. The result in our proposed model has great accuracy for the classification of compounds in the Dipeptidyl Peptidase-4 inhibitors with an accuracy of 86.18%. Furthermore, the values for sensitivity, specificity, and MCC were obtained are 91.05%, 79.45%, and 71.50% respectively.
AB - In recent years, various focusing on Dipeptidyl Peptidase-4 inhibitors drugs discovery to achieve better treatments for type II Diabetes Mellitus. As such, new medical research on new DPP-4 inhibitors with minimal effects is still crucial. One of the drug designs based on in silico is a virtual screening-based ligand (LBVS). The LBVS method used in this research is Quantitative structure-activity relation (QSAR). The QSAR model is a fast and cost-effective alternative for experimental measurement in drug discovery. Deep learning has also been successful and is now widely used in drug discovery. In this study, we propose a combination of two deep learning approaches, namely the Conv1D-LSTM model as a renewable method for predicting the classification of Dipeptidyl Peptidase-4 inhibitors. This model includes the Conv1D model as a data encoding stage and LSTM as a model for the classification of compounds in Dipeptidyl Peptidase-4 inhibitors. We use 2604 molecular structures of DPP-4 inhibitors with 1443 active compounds and 1161 inactive compounds. The result in our proposed model has great accuracy for the classification of compounds in the Dipeptidyl Peptidase-4 inhibitors with an accuracy of 86.18%. Furthermore, the values for sensitivity, specificity, and MCC were obtained are 91.05%, 79.45%, and 71.50% respectively.
KW - Conv1D-LSTM model
KW - Dipeptidyl Peptidase-4 Inhibitors
KW - Drug Discovery
KW - QSAR Classification
UR - http://www.scopus.com/inward/record.url?scp=85113867824&partnerID=8YFLogxK
U2 - 10.1109/AIMS52415.2021.9466083
DO - 10.1109/AIMS52415.2021.9466083
M3 - Conference contribution
AN - SCOPUS:85113867824
T3 - AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems
BT - AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021
Y2 - 28 April 2021 through 30 April 2021
ER -