AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Diversity of Kale (Brassica oleracea var. <i>sabellica</i>): Glucosinolate Content and Phylogenetic Relationships

Recently, kale has become popular due to nutritive components beneficial for human health. It is an important source of phytochemicals such as glucosinolates that trigger associated cancer-preventive activity. However, nutritional value varies among glucosinolates and among cultivars. Here, we start a systematic determination of the content of five glucosinolates in 25 kale varieties and 11 non-kale Brassica oleracea cultivars by HPLC-DAD-ESI-MS<i>ⁿ</i> and compare the profiles with results from the analysis of SNPs derived from a KASP genotyping assay. Our results demonstrate that the glucosinolate levels differ markedly among varieties of different origin. Comparison of the phytochemical data with phylogenetic relationships revealed that the common name kale refers to at least three different groups. German, American, and Italian kales differ morphologically and phytochemically. Landraces do not show outstanding glucosinolate levels. Our results demonstrate the diversity of kale and the importance of preserving a broad genepool for future breeding purposes

Full Stereochemical Determination of Ajudazols A and B by Bioinformatics Gene Cluster Analysis and Total Synthesis of Ajudazol B by an Asymmetric Ortholithiation Strategy

The stereochemical determination of the potent respiratory chain inhibitors ajudazols A and B and the total synthesis of ajudazol B are reported. Configurational assignment was exclusively based on biosynthetic gene cluster analysis of both ketoreductase domains for hydroxyl-bearing stereocenters and one of the first predictive enoylreductase alignments for methyl-bearing stereocenters. The expedient total synthesis resulting in unambiguous proof of the predicted stereochemistry involves a short stereoselective approach to the challenging isochromanone stereotriad by an innovative asymmetric ortholithiation strategy, a modular oxazole formation, and a late-stage <i>Z</i>,<i>Z</i>-selective Suzuki coupling

Total Synthesis and Antibacterial Activity of Dysidavarone A

A concise total synthesis of dysidavarone A possessing the new “dysidavarane” carbon skeleton has been accomplished by a convergent strategy, involving a stereoselective reductive alkylation of a Wieland-Miescher type ketone under Birch conditions and an advantageous intramolecular palladium-catalyzed α-arylation of a sterically hindered ketone. Dysidavarone A showed potent antimicrobial and antiproliferative activities based on characteristic morphological changes of treated cells

Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

Force field-based models are a Newtonian mechanics approximation of reality and are inherently noisy. Coupling models from different molecular scale domains (including single, gas-phase molecules up to multimolecule, condensed phase ensembles) is difficult, which is also the case for finding solutions that transfer well between the scales. In this contribution, we introduce a surrogate-assisted algorithm to optimize Lennard-Jones parameters for target data from different scale domains to overcome the difficulties named above. Specifically, our approach combines a surrogate-assisted global evolutionary optimization method with a presampling phase that takes advantage of one scale domain being less computationally expensive to evaluate. The algorithm’s components were evaluated individually, elucidating their individual merits. Our findings show that the process of parametrizing force fields can significantly benefit from both the presampling method, which alleviates the need to have a good initial guess for the parameters, and the surrogate model, which improves efficiency