Reduced graphs and their applications in chemoinformatics

Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.

Improving graphs of cycles approach to structural similarity of molecules

This paper focuses on determining the structural similarity of two molecules, i.e., the similarity of the interconnection of all the elementary cycles in the corresponding molecular graphs. In this paper, we propose and analyze an algorithmic approach based on the resolution of the Maximum Common Edge Subgraph (MCES) problem with graphs representing the interaction of cycles molecules. Using the ChEBI database, we compare the effectiveness of this approach in terms of structural similarity and computation time with two calculations of similarity of molecular graphs, one based on the MCES, the other on the use of different fingerprints (Daylight, ECFP4, ECFP6, FCFP4, FCFP6) to measure Tanimoto coefficient. We also analyze the obtained structural similarity results for a selected subset of molecules.

Current Status and Future Direction of Fragment-Based Drug Discovery: A Computational Chemistry Perspective

Fragment-based drug discovery (FBDD) has become a well-established and widely used approach for lead identification. The computational chemistry community has played a central role in developing the ideas behind this area of research and computational tools are important throughout FBDD campaigns. This article discusses the evolution of best practice, on-going areas of debate and gaps in current capabilities from a computational chemistry perspective. In particular, the contribution of computational methods to areas such as fragment library design, screening analysis, data handling and the role of structure- and ligand-based design is discussed. The potential to combine FBDD with other hit-identification methods such as high-throughput screening in a more integrated approach is also highlighted.

inSARa: intuitive and interactive SAR interpretation by reduced graphs and hierarchical MCS-based network navigation

The analysis of Structure-Activity-Relationships (SAR) of small molecules is a fundamental task in drug discovery. Although a large number of methods are already published, there is still a strong need for novel intuitive approaches. The inSARa (intuitive networks for Structure-Activity Relationships analysis) method introduced herein takes advantage of the synergistic combination of reduced graphs (RG) and the intuitive maximum common substructure (MCS) concept. The main feature of the inSARa concept is a hierarchical network structure of clearly defined substructure relationships based on common pharmacophoric features. Thus, straightforward SAR interpretation is possible by interactive network navigation. When focusing on a set of active molecules at one single target, the resulting inSARa networks are shown to be valuable for various essential tasks in SAR analysis, such as the identification of activity cliffs or "activity switches", bioisosteric exchanges, common pharmacophoric features, or "SAR hotspots".

Graphs and networks in chemical and biological informatics: past, present and future

Chemical and biological network analysis has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chemistry – predating quantum mechanics – and graphical measures of chemical structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network analysis of large-scale chemical networks, such as protein–protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of ‘one target, one drug’ to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chemical and biological informatics, with particular reference to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chemical network data.

Graph mining: procedure, application to drug discovery and recent advances

Combinatorial chemistry has generated chemical libraries and databases with a huge number of chemical compounds, which include prospective drugs. Chemical structures of compounds can be molecular graphs, to which a variety of graph-based techniques in computer science, specifically graph mining, can be applied. The most basic way for analyzing molecular graphs is using structural fragments, so-called subgraphs in graph theory. The mainstream technique in graph mining is frequent subgraph mining, by which we can retrieve essential subgraphs in given molecular graphs. In this article we explain the idea and procedure of mining frequent subgraphs from given molecular graphs, raising some real applications, and we describe the recent advances of graph mining.