Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

Abstract

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation

“…The training-validation loss ratio could serve as a heuristic to indicate overfitting in some instances, what constitutes a suitable threshold may differ according to the model type and the dataset. Various machine-learning models, especially in intricate architectures such as deep learning, have been found to be a practical approach, even when the ratio between training loss and validation loss is high [47][48][49]. A well-established phenomenon in deep learning, as well as some classical machine learning, has addressed this issue regarding the bias-variance tradeoff, known for the double descent risk curve [50].…”

Lysine-Specific Demethylase 1 Inhibitors: A Comprehensive Review Utilizing Computer-Aided Drug Design Technologies

“…In 2021, Zhou et al utilized a machine learning method to conduct virtual screening of LSD1 inhibitors [ 140 ]. The machine learning model was built based on a database of 931 small molecules with LSD1 inhibitory activity, and the activity predictions were made using Morgan molecular fingerprints.…”

Discovery of novel SOS1 inhibitors using machine learning

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