Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2

6-Aryl-4-cycloamino-1,3,5-triazine-2-amines: synthesis, antileukemic activity, and 3D-QSAR modelling

Despite significant progress in immunotherapy and chimeric antigen receptor T cell therapy of leukemia, chemotherapy is the major treatment option for the disease. Therefore, the development of potent and safe drugs for standard and targeted chemotherapy of leukemia remains an important task for medicinal chemists. A library of 94 diverse 6-aryl-4-cycloamino-1,3,5-triazine-2-amines was prepared using a one-pot microwave-assisted protocol, which involves a three-component reaction of cyanoguanidine, aromatic aldehydes and cyclic amines, and subsequent dehydrogenative aromatization of the dihydrotriazine intermediates in the presence of alkali. The cytotoxic properties of prepared compounds were evaluated against the leukemic Jurkat T cell line and the selectivity of the 24 most active compounds was also assessed using a normal fibroblast MRC-5 cell line, indicating selective antiproliferative activity against leukemic cells. The structure-activity relationship was analysed, and the prepared 3D-QSAR model was found to predict the antileukemic activity of the compounds with reasonable accuracy. In the cell morphology study, both apoptosis and necrosis features were observed in Jurkat T cells after treatment with the most active compound.

Inhibition of SARS-CoV-2 NSP-15 by Uridine-5'-Monophosphate Analogues Using QSAR Modelling, Molecular Dynamics Simulations, and Free Energy Landscape

SARS-CoV-2 is accountable for severe social and economic disruption around the world causing COVID-19. Non-structural protein-15 (NSP15) possesses a domain that is vital to the viral life cycle and is known as uridylate-specific endoribonuclease (EndoU). This domain binds to the uridine 5'-monophosphate (U5P) so that the protein may carry out its native activity. It is considered a vital drug target to inhibit the growth of the virus. Thus, in this current study, ML-based QSAR and virtual screening of U5P analogues targeting Nsp15 were performed to identify potential molecules against SARS-CoV-2. Screening of 816 unique U5P analogues using ML-based QSAR identified 397 compounds ranked on their predicted bioactivity (pIC50). Further, molecular docking and hydrogen bond interaction analysis resulted in the selection of the top three compounds (53309102, 57398422, and 76314921). Molecular dynamics simulation of the most promising compounds showed that two molecules 53309102 and 57398422 acted as potential binders of Nsp15. The compound was able to inhibit nsp15 activity as it was successfully bound to the active site of the nsp15 protein. This was achieved by the formation of relevant contacts with enzymatically critical amino acid residues (His235, His250, and Lys290). Principal component analysis and free energy landscape studies showed stable complex formation while MM/GBSA calculation showed lower binding energies for 53309102 (ΔGTOTAL = -29.4 kcal/mol) and 57398422 (ΔGTOTAL = -39.4 kcal/mol) compared to the control U5P (ΔGTOTAL = -18.8 kcal/mol). This study aimed to identify analogues of U5P inhibiting the NSP15 function that potentially could be used for treating COVID-19.

Cytotoxic Activity of α-Aminophosphonic Derivatives Coming from the Tandem Kabachnik–Fields Reaction and Acylation

Encouraged by the significant cytotoxic activity of simple α-aminophosphonates, a molecular library comprising phosphonoylmethyl- and phosphinoylmethyl-α-aminophosphonates, a tris derivative, and N-acylated species was established. The promising aminophosphonate derivatives were subjected to a comparative structure–activity analysis. We evaluated 12 new aminophosphonate derivatives on tumor cell cultures of different tissue origins (skin, lung, breast, and prostate). Several derivatives showed pronounced, even selective cytostatic effects. According to IC50 values, phosphinoylmethyl-aminophosphonate derivative 2e elicited a significant cytostatic effect on breast adenocarcinoma cells, but it was even more effective against prostatic carcinoma cells. Based on our data, these new compounds exhibited promising antitumor activity on different tumor types, and they might represent a new group of alternative chemotherapeutic agents.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

A New One-Pot Three-Component Synthesis of 4-Aryl-6-cycloamino-1,3,5-triazin-2-amines under Microwave Irradiation

A new method for the fast synthesis of diverse 4-aryl-6-cycloamino-1,3,5-triazin-2-amines was developed. The synthesis is performed under microwave irradiation in a one-pot manner from cyanoguanidine, aromatic aldehydes, and cyclic amines. Their three-component reaction in the presence of hydrochloric acid produced dihydrotriazines, which were then converted (without isolation) into the targeted compounds via aromatic dehydrogenation in the presence of alkali. The reaction tolerated various aromatic aldehydes (including heterocyclic) and cyclic amines. Crystal structures of two representative 4-aryl-6-morpholino-1,3,5-triazin-2-amines were established by X-ray crystallography. The results of preliminary biological screening identified potent antileukemic activity for 6-[3,4-dihydroisoquinolin-2(1H)-yl]-4-phenyl-1,3,5-triazin-2-amine.

Pyrazolyl-pyrimidones inhibit the function of human solute carrier protein SLC11A2 (hDMT1) by metal chelation

Solute carrier proteins (SLCs) control fluxes of ions and molecules across biological membranes and represent an emerging class of drug targets. SLC11A2 (hDMT1) mediates intestinal iron uptake and its inhibition might be used to treat iron overload diseases such as hereditary hemochromatosis. Here we report a micromolar (IC50 = 1.1 μM) pyrazolyl-pyrimidone inhibitor of radiolabeled iron uptake in hDMT1 overexpressing HEK293 cells acting by a non-competitive mechanism, which however does not affect the electrophysiological properties of the transporter. Isothermal titration calorimetry, competition with calcein, induced precipitation of radioactive iron and cross inhibition of the unrelated iron transporter SLC39A8 (hZIP8) indicate that inhibition is mediated by metal chelation. Mapping the chemical space of thousands of pyrazolo-pyrimidones and similar 2,2'-diazabiaryls in ChEMBL suggests that their reported activities might partly reflect metal chelation. Such metal chelating groups are not listed in pan-assay interference compounds (PAINS) but should be checked when addressing SLCs.

Microwave-assisted efficient one-pot synthesis of N 2-(tetrazol-5-yl)-6-aryl/heteroaryl-5,6-dihydro-1,3,5-triazine-2,4-diamines

An efficient one-pot synthesis of N 2-(tetrazol-5-yl)-6-aryl/heteroaryl-1,3,5-triazine-2,4-diamine derivatives was developed by reacting 5-amino-1,2,3,4-tetrazole with aromatic aldehydes and cyanamide in pyridine under controlled microwave heating with high yields. X-ray crystallography confirmed the structure of the obtained products.

ChEMBL-Likeness Score and Database GDBChEMBL

The generated database GDB17 enumerates 166.4 billion molecules up to 17 atoms of C, N, O, S and halogens following simple rules of chemical stability and synthetic feasibility. However, most molecules in GDB17 are too complex to be considered for chemical synthesis. To address this limitation, we report GDBChEMBL as a subset of GDB17 featuring 10 million molecules selected according to a ChEMBL-likeness score (CLscore) calculated from the frequency of occurrence of circular substructures in ChEMBL, followed by uniform sampling across molecular size, stereocenters and heteroatoms. Compared to the previously reported subsets FDB17 and GDBMedChem selected from GDB17 by fragment-likeness, respectively, medicinal chemistry criteria, our new subset features molecules with higher synthetic accessibility and possibly bioactivity yet retains a broad and continuous coverage of chemical space typical of the entire GDB17. GDBChEMBL is accessible at http://gdb.unibe.ch for download and for browsing using an interactive chemical space map at http://faerun.gdb.tools.