Molecular graph augmentation with rings and functional groups

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.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Chemical structure representations and applications in computational toxicity

Efficient storage and retrieval of chemical structures is one of the most important prerequisite for solving any computational-based problem in life sciences. Several resources including research publications, text books, and articles are available on chemical structure representation. Chemical substances that have same molecular formula but several structural formulae, conformations, and skeleton framework/scaffold/functional groups of the molecule convey various characteristics of the molecule. Today with the aid of sophisticated mathematical models and informatics tools, it is possible to design a molecule of interest with specified characteristics based on their applications in pharmaceuticals, agrochemicals, biotechnology, nanomaterials, petrochemicals, and polymers. This chapter discusses both traditional and current state of art representation of chemical structures and their applications in chemical information management, bioactivity- and toxicity-based predictive studies.

Oxygen-containing fragments in natural products

An analysis of the chemical environment of the oxygen atoms in the DNP database compared to the CMC and SCD databases was performed. Some structural clusters were identified which are predominant among the natural products and can be considered as distinctive features of NPs. Fifty-three oxygen-containing structural fragments that are distinctive for the DNP (distinctive set of fragments DSF) in comparison with the SCD have been identified. A new descriptor Mc was introduced for describing the ratio of atoms involved in the DSF to the total number of heavy atoms. A significant difference in the Mc values among the reference databases allowed the use of a specific cluster of the DSF as a tool for performing similarity searches for oxygen-containing NP molecules, or for evaluation or comparison of databases according to their NP-likeness. An example illustrating that the suggested approach could allow not only estimating the NP-likeness, but also serve as a tool for designing new NP-like compounds is provided. The suggested approach for NP-likeness evaluation moves away from the traditional ideas of scaffolds, cycles, linkers and substituents.