Abstract
In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.
Highlights
The discovery of drug candidates capable of blocking or activating the desired target proteins involves extensive virtual and experimental screening that accounts for 30–40% of the total time invested in drug development [1]
The results showed that a large number of useful transformations can be detected by the matched molecular pair analysis (MMPA)-by-QSAR paradigm for driving molecular optimization based on accurate QSAR model predictions
For a better understanding of the effective substructural transformation rules of logP, 16,146 compounds with experimental logP values were collected from ADMETlab [49, 50]
Summary
The discovery of drug candidates capable of blocking or activating the desired target proteins involves extensive virtual and experimental screening that accounts for 30–40% of the total time invested in drug development [1]. The molecular matched pair (MMP) approach, first proposed by Kenny and Sadowski in 2005, has rapidly become a popular method for the extraction of medicinal chemistry knowledge from large compound/property databases, which can be used in a variety of practical applications, such as compound optimization [10, 11]. Compared with DL models, the MMP approach directly deals with measured chemical data and provides a clear interpretation of the results This method allows researchers to directly and extract/summarize information from chemical data and provides a wide range of functions, including suggestions on what compound should be prepared compound property prediction, identification of cases where structural changes have minimal effects on key properties (e.g., bioisosteres), and the simple deepening of our understanding of the links between biology and chemistry [15,16,17,18]. MMPA focuses on local structural transformations rather than the whole molecule and is more suitable for optimization tasks [13]
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