Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey

• Main Authors: Raihana Zahirah, Edros, Feng, Tan wei, Dong, RuiHai
• Format: Article
• Language: English; English
• Published: Taylor & Francis 2023
• Subjects: T Technology (General); TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/42715/1/Utilizing%20machine%20learning%20techniques%20to%20predict%20the%20blood%20brain%20barrier%20permeability%20of%20compounds%20detected%20using%20LCQTOF%20MS%20in%20Malaysian%20Kelulut.pdf; http://umpir.ump.edu.my/id/eprint/42715/2/Utilizing%20machine%20learning%20techniques%20to%20predict%20the%20blood%20brain%20barrier%20permeability%20of%20compounds%20detected%20using%20LCQTOF%20MS%20in%20Malaysian%20Kelulut%20honey.pdf; http://umpir.ump.edu.my/id/eprint/42715/; https://doi.org/10.1080/1062936X.2023.2230868

Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chr...