LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM. - Bilal Shaker; Myeong-Sang Yu; Jin Sook Song; Sunjoo Ahn; Jae Yong Ryu; Kwang-Seok Oh; Dokyun Na (Bioinform., 2021)

In silico methods and tools for drug discovery. - Bilal Shaker; Sajjad Ahmad; Jingyu Lee; Chanjin Jung; Dokyun Na (Comput. Biol. Medicine, 2021)

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds. - Bilal Shaker; Jingyu Lee; Yunhyeok Lee; Myeong-Sang Yu; Hyang-Mi Lee; Eunee Lee; Hoon-Chul Kang; Kwang-Seok Oh; Hyung Wook Kim; Dokyun Na (Bioinform., 2023)

Precision medicine approach for in vitro modeling and computational screening of anti-epileptic drugs in pediatric epilepsy patients with SCN2A (R1629L) mutation. - Jihun Kim; Bilal Shaker; Ara Ko; Sunggon Yoo; Dokyun Na; Hoon-Chul Kang (Comput. Biol. Medicine, 2025)

Employing Automated Machine Learning (AutoML) Methods to Facilitate the In Silico ADMET Properties Prediction. - Herim Han; Bilal Shaker; Jin Hee Lee; Sunghwan Choi; Sanghee Yoon; Maninder Singh; Shaherin Basith; Minghua Cui; Sunil Ahn; Junyoung An; Soosung Kang; Min Sun Yeom; et al. (J. Chem. Inf. Model., 2025)