New Pharmacophore Fingerprints and Weight-matrix Learning for Virtual Screening. Application to Bcr-Abl Data.
Hajar RehiouiBertrand CuissartAbdelkader OualiAlban LepailleurJean-Luc LamotteRonan BureauAlbrecht ZimmermannPublished in: Molecular informatics (2022)
In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight-Matrix Learning (WML, based on a feed-forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR-ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K-Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight-Matrix Learning (SWML R ) leads to clearly superior results in terms of several quality measures.
Keyphrases
- tyrosine kinase
- machine learning
- neural network
- epidermal growth factor receptor
- body mass index
- weight loss
- physical activity
- working memory
- big data
- weight gain
- deep learning
- artificial intelligence
- chronic myeloid leukemia
- body weight
- electronic health record
- molecular docking
- molecular dynamics
- quality improvement
- single cell
- single molecule
- risk assessment