QSAR Classification Modeling for Bioactivity of Molecular Structure via SPL-Logsum

TL;DR

This paper introduces SPL-Logsum, a novel QSAR classification method combining self-paced learning and Logsum regularization to improve bioactivity prediction and descriptor selection in molecular structure analysis.

Contribution

It proposes a new descriptor selection approach using SPL with Logsum penalized logistic regression, enhancing model accuracy and interpretability in QSAR bioactivity prediction.

Findings

Outperforms existing sparse methods in classification accuracy

Selects fewer meaningful molecular descriptors

Demonstrates effectiveness on multiple QSAR datasets

Abstract

Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related bioactivity to molecular structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To address this problem, various of methods have been proposed for descriptor selection. Generally, they can be grouped into three categories: filters, wrappers, and embedded methods. Regularization method is an important embedded technology, which can be used for continuous shrinkage and automatic descriptors selection. In recent years, the interest of researchers in the application of regularization techniques is increasing in descriptors selection , such as, logistic regression(LR) with $L_1$ penalty. In this paper, we proposed a novel descriptor selection method based on self-paced learning(SPL) with Logsum penalized LR for predicting the bioactivity of molecular structure. SPL inspired by the learning process of humans and animals that gradually learns from easy samples(smaller losses) to hard samples(bigger losses) samples into training and Logsum regularization has capacity to select few meaningful and significant molecular descriptors, respectively. Experimental results on simulation and three public QSAR datasets show that our proposed SPL-Logsum method outperforms other commonly used sparse methods in terms of classification performance and model interpretation.