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Abstract
Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.
Highlights
Quantitative Structure-Activity Relationship (QSAR) methods employ the molecular properties of chemical compounds to model biological activity against a target [1]
We illustrate the properties of networks captured by the method along with the regression models generated for each module in the network
Most compounds in the NYPR1 data set are predicted by OPLRAreg within 0.14 to 0.44 log units of accuracy while most predictions made by modSAR vary a little less and range from 0.14 to 0.77 log units (Fig. 5a)
Summary
Quantitative Structure-Activity Relationship (QSAR) methods employ the molecular properties of chemical compounds to model biological activity against a target [1]. In other methods (for example, ensemble methods Random Forest [6] and Extreme Gradient Boosting [7], the modern architectures of artificial neural networks in Deep Learning [8], or in consensus of various machine learning techniques [9]), higher prediction accuracy is achieved at the expense of model interpretability. In this later case, it is difficult or even impossible to trace a relation between molecular descriptors and biological activity in a mechanistically descriptive or interpretable means [10]
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