MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling

Abstract

The use of computer-aided drug design has become an essential part of drug development. In this context, QSAR models capable of predicting biological activities, toxicity, and pharmacokinetic properties were widely used to search bioactive molecules in chemical databases for lead compounds. The preparation of dataset used to build these models has a strong influence on the quality of the generated models, and sampling these data requires that the original dataset be divided into training (used for model training) and test sets (used to further evaluate the predictability of the model). This sampling can be done randomly or rationally, but the rational or purposeful division is superior to the random method. In this paper, we present MASSA Algorithm ("Molecular dAta Set SAmpling Algorithm"), an open-source, user-friendly Python tool that can be used to perform automatic sampling of datasets of molecules into training and test sets by exploring the biological, physicochemical, and structural spaces of molecules using Principal Component Analysis, Hierarchical Clustering Analysis, and K-modes clustering. This proposed algorithm is very useful when the variables for QSAR model generation is not available or to construct multiple QSAR models with the same training and test sets. When compared to random sampling, the presented algorithm produces models with lower variability and higher values across multiple replicates for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, the tool also generates useful graphical representations of the distribution that can provide insights into the data.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

“…The lowest energy conformer for each compound was generated using an OMEGA 3.1.1.2 (OpenEye Scientific Software, USA, 2019) followed by ionization state adjustment at pH 7.4 with FixpKa (QUACPAC 2.0.1.2, OpenEye Scientific Software, USA, 2019), selecting a single favorable ionization state. Finally, the remaining compounds (n = 97) were split into training and test sets (80% and 20%, respectively) using a preliminary version of the MASSA algorithm 51 implemented on the KNIME platform. 52 The most active (lowest half-maximum inhibitory concentration; IC 50 ) compound of each study, herein referred to as 62 29 (IC 50 of 18 nM) and 106 30 (IC 50 of 21 nM), was removed from the test set.…”

MASSA Algorithm: automated rational sampling of training and test subsets for QSAR modelling

“…In the literature, several works can be found describing the construction of predictive models for the most diverse biological activities using different QSAR We present in this paper an open-source, easy-to-use Python tool called MASSA Algorithm ("Molecular dAta Set SAmpling Algorithm") [21] to perform the automatic sampling of datasets of molecules into training and test sets. This algorithm is based on hierarchical clustering analysis of physicochemical and structural spaces, as well as the dependent variables (biological activities).…”

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling

“…In the literature, several works can be found describing the construction of predictive models for the most diverse biological activities using different QSAR We present in this paper an open-source, easy-to-use Python tool called MASSA Algorithm ("Molecular dAta Set SAmpling Algorithm") [28] to perform the automatic sampling of datasets of molecules into training and test sets. This algorithm is based on hierarchical clustering analysis of physicochemical and structural spaces, as well as the dependent variables (biological activities).…”