Method for Systematic Analogue Search Using the Mega SAR Matrix Database

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design

The SAR Matrix (SARM) approach was originally conceived for the systematic identification of analog series, their structural organization, and graphical structure-activity relationship (SAR) analysis. For structurally related series, SARMs also produce virtual candidate compounds. Hence, SARM represents a unique computational approach establishing a direct link between SAR visualization and compound design. The SARM data structure is reminiscent of R-group tables and hence easily accessible from a chemical perspective, although the underlying algorithmic basis is complex. The SARM concept has been extended in different ways to further increase its analytical and design capacity. While the efforts were largely driven from a research perspective, they have also increased the utility for practical applications. Among others, extensions include approaches for SARM-based compound activity prediction, the generation of a large SARM database for analog searching, and the design of a deep learning architecture for advanced analog design taking chemical space information for target families into account. Herein, the SARM approach and its extensions are discussed within their scientific context.

Matched Molecular Series Analysis for ADME Property Prediction

Generation and prioritization of new molecules are the most central part of the drug design process. Matched molecular series analysis (MMSA) has recently been proposed as a formal approach that captures both of these key elements of design. In order to better understand the power of MMSA and its specific limitations, we here evaluate its performance as an ADME property prediction tool. We use four large and diverse inhouse data sets, logD, microsomal clearance, CYP2C9, and CYP3A4 inhibition. MMSA follows the concept of parallel structure-activity relationship (SAR), where if two identical substituent series on different scaffolds show similarity in their property profiles, SAR from one series can be transferred to the other series. We test four different similarity metrics to identify pairs of molecular series where information can be transferred. We find that the best prediction performance is achieved by a combination of centered root-mean-square deviation (cRMSD) and a network score approach previously published by Keefer et al. However, cRMSD alone strikes the best balance between accuracy and the number of predictions that can be made. We identify statistical metrics that allow estimating when MMSA predictions will work, similar to the well-known applicability domain concept in machine learning. MMSA achieves a prediction accuracy that is comparable to a standard machine-learning model and matched molecular pair analysis. In contrast to machine learning, however, it is very easy to understand where MMSA predictions are coming from. Finally, to prospectively test the power of MMSA, we retested compounds that were strong outliers in the initial predictions and show how the MMSA model can help to identify erroneous data points.

Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures

Aim: Enhancing the structure–activity relationship matrix (SARM) methodology through integration of deep learning and expansion of chemical space coverage. Background: Analog design is of critical importance for medicinal chemistry. The SARM approach, which combines systematic structural organization of compound series with analog design, is put into scientific context. Methodology: The new DeepSARM concept is introduced. The architecture of SARM-integrated deep generative models is detailed and the workflow for advanced analog design and matrix expansion described. Exemplary application: The DeepSARM approach is applied to design analogs of kinase inhibitors taking kinome-wide chemical space into account. Future perspective: Practical applications of DeepSARM will be a major focal point. Different applications are discussed. New computational features will be added to prioritize virtual candidate compounds.