A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids

Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new Salmonella mutagenicity dataset with 8,290 unique chemical structures utilizing MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the Salmonella mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest.

A Potential Practical Process for Remdesivir

A four-step synthesis of Remdesivir (1) is presented. This work focuses on the yield improvement of step 1, flow chemistry development of step 2 and 3, process optimization of step 4. The literatures reported (https://dx.doi.org/10.1021/acs.oprd.0c00310 and https://dx.doi.org/10.1021/acs.oprd.0c00172: SI part) step 1 was repeated, but failed in the crystallization, eventually step 1 product was obtained by column chromatography with >98% HPLC purity and 40% IY. The flow chemistry development of step 2(IY: 84%) and 3 (IY: 63%) was achieved and the release of toxic HCN was avoided, the process robustness was improved by flow chemistry. Step 4 was simplified to apply primary alcohol 6 directly (without protection) to react with chiral SM 8. Lewis acid catalysts were screened by HTS and MgI2 gave 50% AY. Cheap and commercially available MgCl2 and NaI were used to replace MgI2, finally Remdesivir (1) was obtained in >99% purity with 40.3% IY.

A Potential Practical Process for Remdesivir

A four-step synthesis of Remdesivir (1) is presented. This work focuses on the yield improvement of step 1, flow chemistry development of step 2 and 3, process optimization of step 4. The literatures reported (https://dx.doi.org/10.1021/acs.oprd.0c00310 and https://dx.doi.org/10.1021/acs.oprd.0c00172: SI part) step 1 was repeated, but failed in the crystallization, eventually step 1 product was obtained by column chromatography with >98% HPLC purity and 40% IY. The flow chemistry development of step 2(IY: 84%) and 3 (IY: 63%) was achieved and the release of toxic HCN was avoided, the process robustness was improved by flow chemistry. Step 4 was simplified to apply primary alcohol 6 directly (without protection) to react with chiral SM 8. Lewis acid catalysts were screened by HTS and MgI2 gave 50% AY. Cheap and commercially available MgCl2 and NaI were used to replace MgI2, finally Remdesivir (1) was obtained in >99% purity with 40.3% IY.

Structural and kinetic characterization of Porphyromonas gingivalis glutaminyl cyclase

Porphyromonas gingivalis is a bacterial species known to be involved in the pathogenesis of chronic periodontitis, that more recently has been as well associated with Alzheimer’s disease. P. gingivalis expresses a glutaminyl cyclase (PgQC) whose human ortholog is known to participate in the beta amyloid peptide metabolism. We have elucidated the crystal structure of PgQC at 1.95 Å resolution in unbound and in inhibitor-complexed forms. The structural characterization of PgQC confirmed that PgQC displays a mammalian fold rather than a bacterial fold. Our biochemical characterization indicates that PgQC uses a mammalian-like catalytic mechanism enabled by the residues Asp149, Glu182, Asp183, Asp218, Asp267 and His299. In addition, we could observe that a non-conserved Trp193 may drive differences in the binding affinity of ligands which might be useful for drug development. With a screening of a small molecule library, we have identified a benzimidazole derivative rendering PgQC inhibition in the low micromolar range that might be amenable for further medicinal chemistry development.

Molecule Set Comparator (MSC): a CDK-based open rich‐client tool for molecule set similarity evaluations

The open rich-client Molecule Set Comparator (MSC) application enables a versatile and fast comparison of large molecule sets with a unique inter-set molecule-to-molecule mapping obtained e.g. by molecular-recognition-oriented machine learning approaches. The molecule-to-molecule comparison is based on chemical descriptors obtained with the Chemistry Development Kit (CDK), such as Tanimoto similarities, atom/bond/ring counts or physicochemical properties like logP. The results are summarized and presented graphically by interactive histogram charts that can be examined in detail and exported in publication quality.

Hit-optimization using target-directed dynamic combinatorial chemistry: Development of inhibitors of the anti-infective target 1-deoxy-D-xylulose-5-phosphate synthase

Target-directed dynamic combinatorial chemistry (tdDCC) enables identification, as well as optimization of ligands for un(der)explored targets such as the anti-infective target 1-deoxy-D-xylulose-5-phosphate synthase (DXPS). We report the use of tdDCC...

Hit-Optimization Using Target-Directed Dynamic Combinatorial Chemistry: Development of Inhibitors of the Anti-Infective Target 1-Deoxy-D-Xylulose-5-Phosphate Synthase

<p>Target-directed dynamic combinatorial chemistry (tdDCC) enables the identification, as well as optimization of ligands for un(der)explored targets such as the anti-infective target 1‑deoxy‑d‑xylulose-5-phosphate synthase (DXS). We report the unprecedented use of tdDCC to first identify and subsequently optimize inhibitors of the anti-infective target DXS. Using tdDCC, we were able to generate acylhydrazone-based inhibitors for DXS. The tailored tdDCC runs also provided insights into the structure–activity relationship of this novel class of DXS inhibitors. This approach holds the potential to expedite the drug discovery process and could be generally applied to a range of biological targets.</p>

Hit-Optimization Using Target-Directed Dynamic Combinatorial Chemistry: Development of Inhibitors of the Anti-Infective Target 1-Deoxy-D-Xylulose-5-Phosphate Synthase

<p>Target-directed dynamic combinatorial chemistry (tdDCC) enables the identification, as well as optimization of ligands for un(der)explored targets such as the anti-infective target 1‑deoxy‑d‑xylulose-5-phosphate synthase (DXS). We report the unprecedented use of tdDCC to first identify and subsequently optimize inhibitors of the anti-infective target DXS. Using tdDCC, we were able to generate acylhydrazone-based inhibitors for DXS. The tailored tdDCC runs also provided insights into the structure–activity relationship of this novel class of DXS inhibitors. This approach holds the potential to expedite the drug discovery process and could be generally applied to a range of biological targets.</p>