title: Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits.
abstract: The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool 'Emergent Self-Organizing Maps' for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.
authors: Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia. saleh_ihmaid@yahoo.com.au.|School of Pharmacy and Applied Science, La Trobe University, P. O. Box 199, Bendigo 3552, Australia. saleh_ihmaid@yahoo.com.au.
authors: Saleh K
authors: SK
authors: Ihmaid
authors: Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia. heahmad@taibahu.edu.sa.|Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Al-Azhar University, P. O. Box 11651, Cairo 11884, Egypt. heahmad@taibahu.edu.sa.
authors: Hany E A
authors: HE
authors: Ahmed
authors: Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia. mfzayed25@yahoo.com.|Pharmaceutical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, P. O. Box 11651, Cairo 11884, Egypt. mfzayed25@yahoo.com.
authors: Mohamed F
authors: MF
authors: Zayed
authors: Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia. mommetto@hotmail.com.
authors: Mohammed M
authors: MM
authors: Abadleh
mesh_terms: D056657:Cathepsin K / Q000037:antagonists & inhibitors*; D002403:Cathepsins / Q000037:antagonists & inhibitors*; D002965:Classification; D019295:Computational Biology / Q000379:methods*; D062126:Databases, Chemical; D055808:Drug Discovery; D006801:Humans; D000069550:Machine Learning; D011485:Protein Binding; D011487:Protein Conformation; D013329:Structure-Activity Relationship
chemical_list: D002403:Cathepsins; C028217:cathepsin S; D056657:Cathepsin K
keywords: cathepsin inhibitors; clustering; fingerprints; selectivity; self-organizing map (SOM)