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Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six techniques to mine these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against was assessed using a comprehensive biological study. Kohonen-based nonlinear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors and . For the best compounds, MIC and CC values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

The present report describes our efforts to identify new structural classes of compounds having promising antibacterial activity using previously published double-reporter system pDualrep2. This semi-automated high-throughput screening (HTS) platform has been applied to perform a large-scale screen of a diverse small-molecule compound library. We have selected a set of more than 125,000 molecules and evaluated them for their antibacterial activity. On the basis of HTS results, eight compounds containing 2-pyrazol-1-yl-thiazole scaffold exhibited moderate-to-high activity against ΔTolC Escherichia coli. Minimum inhibitory concentration (MIC) values for these molecules were in the range of 0.037–8 μg ml−1. The most active compound 8 demonstrated high antibacterial potency (MIC = 0.037 μg ml−1), that significantly exceed that measured for erythromycin (MIC = 2.5 μg ml−1) and was comparable with the activity of levofloxacin (MIC = 0.016 μg ml−1). Unfortunately, this compound showed only moderate selectivity toward HEK293 eukaryotic cell line. On the contrary, compound 7 was less potent (MIC = 0.8 μg ml−1) but displayed only slight cytotoxicity. Thus, 2-pyrazol-1-yl-thiazoles can be considered as a valuable starting point for subsequent optimization and morphing.

A series of 5-oxo-4H-pyrrolo[3,2-b]pyridine derivatives was identified as novel class of highly potent antibacterial agents during an extensive large-scale high-throughput screening (HTS) program utilizing a unique double-reporter system—pDualrep2. The construction of the reporter system allows us to perform visual inspection of the underlying mechanism of action due to two genes—Katushka2S and RFP—which encode the proteins with different imaging signatures. Antibacterial activity of the compounds was evaluated during the initial HTS round and subsequent rescreen procedure. The most active molecule demonstrated a MIC value of 3.35 µg/mL against E. coli with some signs of translation blockage (low Katushka2S signal) and no SOS response. The compound did not demonstrate cytotoxicity in standard cell viability assay. Subsequent structural morphing and follow-up synthesis may result in novel compounds with a meaningful antibacterial potency which can be reasonably regarded as an attractive starting point for further in vivo investigation and optimization.

5-Hydroxytryptamine (5-HT, serotonin) subtype 6 receptor (5-HT receptor, 5-HT R) belongs to a 5-HT subclass of a relatively wide G protein-coupled receptor (GPCR) family. Accumulated biological data indicate that 5-HT R antagonists and agonists have a great potential for the treatment of neuropathological disorders, such as Parkinson’s disease, Alzheimer’s disease, and schizophrenia. A number of painstaking efforts have been made toward the design of novel 5-HT R ligands; however, there are still no drugs that successfully passed all the clinical trials and entered the market, except for several multimodal ligands. Novel active molecules are strongly needed to progress this development forward. The drug design has some benefits compared with the other rough approaches in terms of thoroughness and predictive accuracy; therefore, it can be effectively used as a solid foundation for the design of novel 5-HT R ligands with high potency and selectivity. Here, we provide an overview of the reported computational approaches to the design of novel 5-HT R ligands.

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice. A machine learning model allows the identification of new small-molecule kinase inhibitors in days.

5-Hydroxytryptamine (5-HT, serotonin) subtype 6 receptor (5-HT 6 receptor, 5-HT 6 R) belongs to a 5-HT subclass of a relatively wide G protein-coupled receptor (GPCR) family. Accumulated biological data indicate that 5-HT 6 R antagonists and agonists have a great potential for the treatment of neuropathological disorders, such as Parkinson's disease, Alzheimer's disease, and schizophrenia. A number of painstaking efforts have been made toward the design of novel 5-HT 6 R ligands; however, there are still no drugs that successfully passed all the clinical trials and entered the market, except for several multimodal ligands. Novel active molecules are strongly needed to progress this development forward. The in silico drug design has some benefits compared with the other rough approaches in terms of thoroughness and predictive accuracy; therefore, it can be effectively used as a solid foundation for the design of novel 5-HT 6 R ligands with high potency and selectivity. Here, we provide an overview of the reported computational approaches to the design of novel 5-HT 6 R ligands.

Extensive biochemical and clinical studies have increasingly recognized Parkinson’s disease as a highly complex and multi-faceted neurological disorder having branched non-motor symptoms including sleep disorders, pain, constipation, psychosis, depression, and fatigue. A wide range of biological targets in the brain deeply implicated in this pathology resulted in a plethora of novel small-molecule compounds with promising activity. This review thoroughly describes the chemical space of non-dopamine receptor ligands in terms of diversity, isosteric/bioisosteric morphing, and molecular descriptors.

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses .

A series of novel S-, O- and Se-containing dispirooxindole derivatives has been synthesized using 1,3-dipolar cycloaddition reaction of azomethine ylide generated from isatines and sarcosine at the double C=C bond of 5-indolidene-2-chalcogen-imidazolones (chalcogen was oxygen, sulfur or selenium). The cytotoxicity of these dispiro derivatives was evaluated in vitro using different tumor cell lines. Several molecules have demonstrated a considerable cytotoxicity against the panel and showed good selectivity towards colorectal carcinoma HCT116 p53+/+ over HCT116 p53−/− cells. In particular, good results have been obtained for LNCaP prostate cell line. The performed in silico study has revealed MDM2/p53 interaction as one of the possible targets for the synthesized molecules. However, in contrast to selectivity revealed during the cell-based evaluation and the results obtained in computational study, no significant p53 activation using a reporter construction in p53wt A549 cell line was observed in a relevant concentration range.

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervised predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides a training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses.