Phuong Linh Nguyen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Interests
Phuong Linh Nguyen has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Bakhtiyor Rasulev
Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
1 shared publication - Daniel Baecker
Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Straße 2+4, 14195 Berlin, Germany
1 shared publication - Do Thi Mai Dung
Hanoi University of Pharmacy, Le Thanh Tong 13-15, Hanoi 11100, Vietnam
1 shared publication - Gerardo Maikel Casañola-Martin
Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
1 shared publication - Hai Pham-The
Department of Medicine and Pharmacy, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi 100000, Vietnam
1 shared publication - Le Quang Bao
Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
1 shared publication
Latest Publications
- Open Access
Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer’s Disease
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer’s disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.
Open AccessDevelopment of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer’s Disease
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer’s disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.