KNIME-CDK: Workflow-driven cheminformatics

BACKGROUND: Cheminformaticians have to routinely process and analyse libraries of small molecules. Among other things, that includes the standardization of molecules, calculation of various descriptors, visualisation of molecular structures, and downstream analysis. For this purpose, scientific workflow platforms such as the Konstanz Information Miner can be used if provided with the right plug-in. A workflow-based cheminformatics tool provides the advantage of ease-of-use and interoperability between complementary cheminformatics packages within the same framework, hence facilitating the analysis process. RESULTS: KNIME-CDK comprises functions for molecule conversion to/from common formats, generation of signatures, fingerprints, and molecular properties. It is based on the Chemistry Development Toolkit and uses the Chemical Markup Language for persistence. A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK supports a similar range of chemical classes and adds new functionality to the framework. We describe the design and integration of the plug-in, and demonstrate the usage of the nodes on ChEBI, a library of small molecules of biological interest. CONCLUSIONS: KNIME-CDK is an open-source plug-in for the Konstanz Information Miner, a free workflow platform. KNIME-CDK is build on top of the open-source Chemistry Development Toolkit and allows for efficient cross-vendor structural cheminformatics. Its ease-of-use and modularity enables researchers to automate routine tasks and data analysis, bringing complimentary cheminformatics functionality to the workflow environment

Molecular, spectroscopic, and magnetic properties of cobalt(II) complexes with heteroaromatic N(O)-donor ligands

New [Co(SCN)2(L)4/2] complexes, where L = b-pic (1), pyCH2OH (2), py(CH2)3OH (3), 1,2,4- triazolo[1,5-a]pyrimidine (4), [CoCl2(urotrop)2] (5), and [Co(DMIM)3]Cl2 H2O (6) where urotrop = hexamethylenetetramine and DMIM = 2,20-bis(4,5-dimethylimidazolyl) were synthesized in simple reactions of CoCl2 6H2O with ammonia thiocyanate and pyridine type ligands or urotropine and diimidazolyl ligands with cobalt(II) chloride in methanol solutions. The orthorhombic crystallization for (1), (2), and (4), the monoclinic one for (3) and (5) as well as the hexagonal one for (6) were found. The plots of the overlap population density-of-states indicated nonbonding character of the interactions between pyridine derivatives ligands and cobalt(II) ions in the complexes (1)–(4). The electronic spectra showed almost perfect octahedral complex in the case of (6). The magnetic susceptibility measurements revealed paramagnetic behavior with low values of the Curie–Weiss temperature, positive for complex (5) and negative for the other ones, although the transition to collective magnetic state at low temperatures for (4) and (5) was evidenced by an observation of antiferromagnetic coupling with Ne´el temperature of 4.5 K and the ferromagnetic one with Curie temperature of 10 K, respectively

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction

Magnetic and Photoluminescent Sensors Based on Metal-Organic Frameworks Built up from 2-aminoisonicotinate

Red Guipuzcoana de Ciencia, Tecnologia e Innovacion OF218/2018 University of Basque Country GIU 17/13 Basque Government IT1005-16 IT1291-19 IT1310-19 Junta de Andalucia FQM-394 Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) PGC2018-102052-A-C22 PGC2018-102052-B-C21 MAT2016-75883-C2-1-P European Union (EU) ESFIn this work, three isostructural metal-organic frameworks based on frst row transition metal ions and 2-aminoisonicotinate (2ain) ligands, namely, {[M(μ-2ain)2]·DMF}n [MII=Co (1), Ni (2), Zn (3)], are evaluated for their sensing capacity of various solvents and metal ions by monitoring the modulation of their magnetic and photoluminescence properties. The crystal structure consists of an open diamond-like topological 3D framework that leaves huge voids, which allows crystallizing two-fold interpenetrated architecture that still retains large porosity. Magnetic measurements performed on 1 reveal the occurrence of feld-induced spin-glass behaviour characterized by a frequency-independent relaxation. Solvent-exchange experiments lead successfully to the replacement of lattice molecules by DMSO and MeOH, which, on its part, show dominating SIM behaviour with low blocking temperatures but substantially high energy barriers for the reversal of the magnetization. Photoluminescence studied at variable temperature on compound 3 show its capacity to provide bright blue emission under UV excitation, which proceeds through a ligand-centred charge transfer mechanism as confrmed by timedependent DFT calculations. Turn-of and/or shift of the emission is observed for suspensions of 3 in diferent solvents and aqueous solutions containing metal ions