Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints

Prediction of whether a compound is "aromatic" is at first glance a relatively simple task-does it obey Hückel's rule (planar cyclic π-system with 4n + 2 electrons) or not? However, aromaticity is far from a binary property, and there are distinct variations in the chemical and biological behavior of different systems which obey Hückel's rule and are thus classified as aromatic. To that end, the aromaticity of each molecule in a large public dataset was quantified by an extension of the work of Raczyńska et al. Building on this data, a method is proposed for machine learning the degree of aromaticity of each aromatic ring in a molecule. Categories are derived from the numeric results, allowing the differentiation of structural patterns between them and thus a better representation of the underlying chemical and biological behavior in expert and (Q)SAR systems.

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequently trained using canonical SMILES. In this study, we introduce Generative Examination Networks (GEN) as a new approach to train deep generative networks for SMILES generation. In our GENs, we have used an architecture based on multiple concatenated bidirectional RNN units to enhance the validity of generated SMILES. GENs autonomously learn the target space in a few epochs and are stopped early using an independent online examination mechanism, measuring the quality of the generated set. Herein we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95–98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our trained models combine an excellent novelty rate (85–90%) while generating SMILES with strong conservation of the property space (95–99%). In GENs, both the generative network and the examination mechanism are open to other architectures and quality criteria.

Optimization of TRPV6 Calcium Channel Inhibitors Using a 3D Ligand-Based Virtual Screening Method

Herein, we report the discovery of the first potent and selective inhibitor of TRPV6, a calcium channel overexpressed in breast and prostate cancer, and its use to test the effect of blocking TRPV6-mediated Ca(2+)-influx on cell growth. The inhibitor was discovered through a computational method, xLOS, a 3D-shape and pharmacophore similarity algorithm, a type of ligand-based virtual screening (LBVS) method described briefly here. Starting with a single weakly active seed molecule, two successive rounds of LBVS followed by optimization by chemical synthesis led to a selective molecule with 0.3 μM inhibition of TRPV6. The ability of xLOS to identify different scaffolds early in LBVS was essential to success. The xLOS method may be generally useful to develop tool compounds for poorly characterized targets.

MQN-mapplet: visualization of chemical space with interactive maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13

The MQN-mapplet is a Java application giving access to the structure of small molecules in large databases via color-coded maps of their chemical space. These maps are projections from a 42-dimensional property space defined by 42 integer value descriptors called molecular quantum numbers (MQN), which count different categories of atoms, bonds, polar groups, and topological features and categorize molecules by size, rigidity, and polarity. Despite its simplicity, MQN-space is relevant to biological activities. The MQN-mapplet allows localization of any molecule on the color-coded images, visualization of the molecules, and identification of analogs as neighbors on the MQN-map or in the original 42-dimensional MQN-space. No query molecule is necessary to start the exploration, which may be particularly attractive for nonchemists. To our knowledge, this type of interactive exploration tool is unprecedented for very large databases such as PubChem and GDB-13 (almost one billion molecules). The application is freely available for download at www.gdb.unibe.ch.

Exploring α7-Nicotinic Receptor Ligand Diversity by Scaffold Enumeration from the Chemical Universe Database GDB

Virtual analogues (1167860 compounds) of the nicotinic α7-receptor (α7 nAChR) ligands PNU-282,987 and SSR180711 were generated from the chemical universe database GDB-11 by extracting all aliphatic diamine analogues of the aminoquinuclidine and 1,4-diazabicyclo[3.2.2]nonane scaffolds of these ligands and converting them to the corresponding aryl amides using five different aromatic acyl groups. The library was ranked by docking to the nicotinic binding site of the acetylcholine binding protein (AChBP, 1UW6.pdb) using Autodock and Glide. Thirty-eight ligands derived from the best docking hits were synthesized and tested for modulation of the acetylcholine signal at the human α7 nAChR receptor expressed in Xenopus oocytes, leading to competitive and noncompetitive antagonists with IC50 = 5-7 μM. These experiments demonstrate the first example of using GDB in a fragment-based approach by diversifying the scaffold of known drugs.

A putative consensus sequence for the nucleotide-binding site of annexin A6

Reaction-induced infrared difference spectroscopy (RIDS) has been used to investigate the nature of interactions of human annexin A6 (ANXA6) with nucleotides. RIDS results for ANXA6, obtained after the photorelease of GTP-gamma-S, ATP, or P(i) from the respective caged compounds, were identical, suggesting that the interactions between the nucleotide and ANXA6 were dominated by the phosphate groups. Phosphate-induced structural changes in ANXA6 were small and affected only seven or eight amino acid residues. The GTP fluorescent analogue, 2'(3')-O-(2,4,6-trinitrophenyl)guanosine 5'-triphosphate (TNP-GTP), quenched tryptophan fluorescence of ANXA6 when bound to the protein. A binding stoichiometry of 1 mol of nucleotide/mol ANXA6 was established with a K(D) value of 2.8 microM for TNP-GTP. The bands observed on RIDS of ANXA6 halves (e.g., N-terminal half, ANXA6a, and C-terminal half, ANXA6b) were similar to those of the whole molecule. However, their amplitudes were smaller by a factor of 2 compared to those of whole ANXA6. TNP-GTP bound to both fragments of ANXA6 with a stoichiometry of 0.5 mol/mol. However, the binding affinities of ANXA6a and ANXA6b differed from that of ANXA6. Simulated molecular modeling revealed a nucleotide-binding site which was distributed in two distinct domains. Residues K296, Y297, K598, and K644 of ANXA6 were less than 3 A from the bound phosphate groups of either GTP or ATP. The presence of two identical sequences in ANXA6 with the F-X-X-K-Y-D/E-K-S-L motif, located in the middle of ANXA6, at residues 293-301 (within ANXA6a) and at 641-649 (within ANXA6b), suggested that the F-X-X-K-Y-D/E-K-S-L motif was the putative sequence in ANXA6 for nucleotide binding.