可解释性
指纹(计算)
计算机科学
人工智能
虚拟筛选
Python(编程语言)
化学信息学
机器学习
代表(政治)
模式识别(心理学)
药物发现
生物信息学
生物
政治
操作系统
法学
政治学
作者
Alexandre Victor Fassio,Laura Shub,Luca Ponzoni,Jessica L McKinley,Matthew J. O’Meara,Rafaela Salgado Ferreira,Michael J. Keiser,Raquel C. de Melo-Minardi
标识
DOI:10.1021/acs.jcim.2c00695
摘要
Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints' use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Second, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints overlook. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.
科研通智能强力驱动
Strongly Powered by AbleSci AI