HDAC3i‐Finder: A Machine Learning‐based Computational Tool to Screen for HDAC3 Inhibitors

Abstract

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two‐dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k‐Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best‐performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well‐curated benchmarking set named MUBD‐HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel‐scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i‐Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i‐Finder is available at https://github.com/jwxia2014/HDAC3i‐Finder.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds

“…The Y‐Randomization test is performed to test the robustness of a ML model [36,37] . In this work, we have performed the Y‐Randomization test on the models which performed best among all others.…”

“…The Y-Randomization test is performed to test the robustness of a ML model. [36,37] In this work, we have performed the Y-Randomization test on the models which performed best among all others. For each such model, the test was performed thrice with the datasets from all the three feature selection methods.…”

Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification

“…Sun et al [ 30 ] performed a QSAR and classification study based on a total of 134 base analogs related to their ED50 values. Li et al [ 31 ] trained five machine learning classifiers, that is, K-nearest neighbor (KNN), SVM, random forest (RF), XGBoost, and DNN on each feature set of histone deacetylase 3 to facilitate prospective screening for inhibitors. Zhang et al [ 32 ] used the genetic algorithm to select important molecular descriptors and used the NB for the in silico prediction model.…”

Epigenetic Target Fishing with Accurate Machine Learning Models

“…25,27,30 For instance, in a retrospective large-scale comparison of machine learning methods for target prediction on ChEMBL (in the context of biochemical assays), 36 deep neural networks were found to be the best performing method for this task when trained on Extended Connectivity Fingerprints 37 (ECFP) of chemical compounds. 38 However, the application of machine learning models for large-scale epigenetic target prediction has been explored on a limited basis, with most works being focused on single targets 39,40 or protein families such as HDACs 41 or the BET family. 42 Herein, we aimed to develop accurate models for epigenetic target fishing based on well-established machine learning algorithms trained on different fingerprint representations of compounds.…”

“…Machine learning methods have proven to be useful in multiple areas of drug discovery, − one such being target prediction of small molecules. ,, For instance, in a retrospective large-scale comparison of machine learning methods for target prediction on ChEMBL (in the context of biochemical assays), deep neural networks were found to be the best performing method for this task when trained on Extended Connectivity Fingerprints (ECFP) of chemical compounds . However, the application of machine learning models for large-scale epigenetic target prediction has been explored on a limited basis, with most works being focused on single targets , or protein families such as HDACs or the BET family …”