QComp: A QSAR-Based Data Completion Framework for Drug Discovery
<|reference_start|>QComp: A QSAR-Based Data Completion Framework for Drug Discovery: 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.<|reference_end|>
@article{yang2024qcomp:,
    title={QComp: A QSAR-Based Data Completion Framework for Drug Discovery},
    author={Bingjia Yang, Yunsie Chung, Archer Y. Yang, Bo Yuan, Xiang Yu},
    journal={arXiv preprint arXiv:2405.11703},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11703},
    primaryClass={cs.LG}
}