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.

基于已有的 QSAR 模型，QComp 利用实验数据中固有的相关性，提高了各种任务的预测准确性，同时通过量化特定终点的统计不确定性的减少，为合理决策提供支持，从而成为引导药物发现过程中最佳实验顺序的有前途工具。