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Abstract Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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Knowledge transfer between small datasets for boosting the predictive performance of machine learning assisted QSAR models on contaminant oxidative reactivity
Using machine learning (ML) to develop quantitative structure—activity relationship (QSAR) models for contaminant reactivity has emerged as a promising approach because it can effectively handle non-linear relationships. However, ML is often data-demanding, whereas data scarcity is common in QSAR model development. Here, we proposed two approaches to address this issue: combining small datasets and transferring knowledge between them. First, we compiled four individual datasets for four oxidants, i.e., SO4•-, HClO, O3 and ClO2, each dataset containing a different number of contaminants with their corresponding rate constants and reaction conditions (pH and/or temperature). We then used molecular fingerprints (MF) or molecular descriptors (MD) to represent the contaminants; combined them with ML algorithms to develop individual QSAR models for these four datasets; and interpreted the models by the Shapley Additive exPlantion (SHAP) method. The results showed that both the optimal contaminant representation and the best ML algorithm are dataset dependent. Next, we merged these four datasets and developed a unified model, which showed better predictive performance on the datasets of HClO, O3 and ClO2 because the model ‘corrected’ some wrongly learned effects of several atom groups. We further developed knowledge transfer models based on the second approach, the effectiveness of which depends on if there is consistent knowledge shared between the two datasets as well as the predictive performance of the respective single models. This study demonstrated the benefit of combining small similar datasets and transferring knowledge between them, which can be leveraged to boost the predictive performance of ML-assisted QSAR models.
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- Award ID(s):
- 1808406
- PAR ID:
- 10288084
- Date Published:
- Journal Name:
- Environmental science technology
- Volume:
- In review
- ISSN:
- 1086-931X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Quantitative structure–activity relationship (QSAR) modeling has become a critical tool in drug design. Recently proposed Topological Regression (TR), a computationally efficient and highly interpretable QSAR model that maps distances in the chemical domain to distances in the activity domain, has shown predictive performance comparable to state-of-the-art deep learning-based models. However, TR’s dependence on simple random sampling-based anchor selection and utilization of radial basis function for response reconstruction constrain its interpretability and predictive capacity. To address these limitations, we propose Adaptive Topological Regression (AdapToR) with adaptive anchor selection and optimization-based reconstruction. We evaluated AdapToR on the NCI60 GI50 dataset, which consists of over 50,000 drug responses across 60 human cancer cell lines, and compared its performance to Transformer CNN, Graph Transformer, TR, and other baseline models. The results demonstrate that AdapToR outperforms competing QSAR models for drug response prediction with significantly lower computational cost and greater interpretability as compared to deep learning-based models.more » « less
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