Piperine activates TRPV1 (transient receptor potential vanilloid type 1 receptor) receptors and modulates γ-aminobutyric acid type A receptors (GABAAR). We have synthesized a library of 76 piperine analogues and analyzed their effects on GABAAR by means of a two-microelectrode voltage-clamp technique. GABAAR were expressed in Xenopus laevis oocytes. Structure–activity relationships (SARs) were established to identify structural elements essential for efficiency and potency. Efficiency of piperine derivatives was significantly increased by exchanging the piperidine moiety with either N,N-dipropyl, N,N-diisopropyl, N,N-dibutyl, p-methylpiperidine, or N,N-bis(trifluoroethyl) groups. Potency was enhanced by replacing the piperidine moiety by N,N-dibutyl, N,N-diisobutyl, or N,N-bistrifluoroethyl groups. Linker modifications did not substantially enhance the effect on GABAAR. Compound 23 [(2E,4E)-5-(1,3-benzodioxol-5-yl)-N,N-dipropyl-2,4-pentadienamide] induced the strongest modulation of GABAA (maximal GABA-induced chloride current modulation (IGABA-max = 1673% ± 146%, EC50 = 51.7 ± 9.5 μM), while 25 [(2E,4E)-5-(1,3-benzodioxol-5-yl)-N,N-dibutyl-2,4-pentadienamide] displayed the highest potency (EC50 = 13.8 ± 1.8 μM, IGABA-max = 760% ± 47%). Compound 23 induced significantly stronger anxiolysis in mice than piperine and thus may serve as a starting point for developing novel GABAAR modulators.

The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.

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Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.

Open-source workflows have become more and more an integral part of computer-aided drug design (CADD) projects since they allow reproducible and shareable research that can be easily transferred to other projects. Setting up, understanding, and applying such workflows involves either coding or using workflow managers that offer a graphical user interface. We previously reported the TeachOpenCADD teaching platform that provides interactive Jupyter Notebooks (talktorials) on central CADD topics using open-source data and Python packages. Here we present the conversion of these talktorials to KNIME workflows that allow users to explore our teaching material without any line of code. TeachOpenCADD KNIME workflows are freely available on the KNIME Hub: https://hub.knime.com/volkamerlab/space/TeachOpenCADD.