QComp: A QSAR-Based Data Completion Framework for Drug Discovery

TL;DR

QComp is a novel framework that enhances QSAR models for drug discovery by improving data completion and guiding experimental sequences through uncertainty quantification.

Contribution

It introduces QComp, which leverages data correlations to improve prediction accuracy and optimize experimental planning in evolving drug discovery datasets.

Findings

Improves prediction accuracy in sparse, evolving datasets

Guides experimental sequencing by quantifying uncertainty

Enhances decision-making in drug discovery processes

Abstract

In drug discovery, in vitro and in vivo experiments reveal biochemical activities related to the efficacy and toxicity of compounds. The experimental data accumulate into massive, ever-evolving, and sparse datasets. Quantitative Structure-Activity Relationship (QSAR) models, which predict biochemical activities using only the structural information of compounds, face challenges in integrating the evolving experimental data as studies progress. We develop QSAR-Complete (QComp), a data completion framework to address this issue. Based on pre-existing QSAR models, QComp utilizes the correlation inherent in experimental data to enhance prediction accuracy across various tasks. Moreover, QComp emerges as a promising tool for guiding the optimal sequence of experiments by quantifying the reduction in statistical uncertainty for specific endpoints, thereby aiding in rational decision-making throughout the drug discovery process.