Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Identification of potential inhibitors of casein kinase 2 alpha of Plasmodium falciparum with potent in vitro activity

Drug resistance to commercially available antimalarials is a major obstacle in malaria control and elimination, creating the need to find new antiparasitic compounds with novel mechanisms of action. The success of kinase inhibitors for oncological treatments has paved the way for the exploitation of protein kinases as drug targets in various diseases, including malaria. Casein kinases are ubiquitous serine/threonine kinases involved in a wide range of cellular processes such as mitotic checkpoint signaling, DNA damage response, and circadian rhythm. In Plasmodium, it is suggested that these protein kinases are essential for both asexual and sexual blood-stage parasites, reinforcing their potential as targets for multi-stage antimalarials. To identify new putative PfCK2α inhibitors, we utilized an in silico chemogenomic strategy involving virtual screening with docking simulations and quantitative structure-activity relationship predictions. Our investigation resulted in the discovery of a new quinazoline molecule (542), which exhibited potent activity against asexual blood stages and a high selectivity index (>100). Subsequently, we conducted chemical-genetic interaction analysis on yeasts with mutations in casein kinases. Our chemical-genetic interaction results are consistent with the hypothesis that 542 inhibits yeast Cka1, which has a hinge region with high similarity to PfCK2α. This finding is in agreement with our in silico results suggesting that 542 inhibits PfCK2α via hinge region interaction.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Antitrypanosomal Activity of Acetogenins Isolated from the Seeds of Porcelia macrocarpa Is Associated with Alterations in Both Plasma Membrane Electric Potential and Mitochondrial Membrane Potential

As part of a drug discovery program aimed at the identification of anti- Trypanosoma cruzi metabolites from Brazilian flora, four acetogenins (1-4) were isolated from the seeds of Porcelia macrocarpa and were identified by NMR spectroscopy and HRESIMS. The new compounds 1 and 2 displayed activity against the trypomastigote (IC₅₀ = 0.4 and 3.6 μM) and amastigote (IC₅₀ = 23.0 and 27.7 μM) forms. The structurally related known compound 3 showed less potency to the amastigotes, with an IC₅₀ value of 58 μM, while the known compound 4 was inactive. To evaluate the potential mechanisms for parasite death, parameters were evaluated by fluorometric assays: (i) plasma membrane permeability, (ii) plasma membrane electric potential (ΔΨ_p), (iii) reactive oxygen species production, and (iv) mitochondrial membrane potential (ΔΨ_m). The results obtained indicated that compounds 1 and 2 depolarize plasma membranes, affecting ΔΨ_p and ΔΨ_m and contributing to the observed cellular damage and disturbing the bioenergetic system. In silico studies of pharmacokinetics and toxicity (ADMET) properties predicted that all compounds were nonmutagenic, noncarcinogenic, nongenotoxic, and weak hERG blockers. Additionally, none of the isolated acetogenins 1-4 were predicted as pan-assay interference compounds.