Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum

Design, Synthesis, Antitumor, and Antiplasmodial Evaluation of New 7-Chloroquinoline-Benzimidazole Hybrids

Newly synthesized 7-chloro-4-aminoquinoline-benzimidazole hybrids were characterized by NMR and elemental analysis. Compounds were tested for their effects on the growth of the non-tumor cell line MRC-5 (human fetal lung fibroblasts) and carcinoma (HeLa and CaCo-2), leukemia, and lymphoma (Hut78, THP-1, and HL-60) cell lines. The obtained results, expressed as the concentration at which 50% inhibition of cell growth is achieved (IC50 value), show that the tested compounds affect cell growth differently depending on the cell line and the applied dose (IC50 ranged from 0.2 to >100 µM). Also, the antiplasmodial activity of these hybrids was evaluated against two P. falciparum strains (Pf3D7 and PfDd2). The tested compounds showed potent antiplasmodial activity, against both strains, at nanomolar concentrations. Quantitative structure-activity relationship (QSAR) analysis resulted in predictive models for antiplasmodial activity against the 3D7 strain (R2 = 0.886; Rext2 = 0.937; F = 41.589) and Dd2 strain (R2 = 0.859; Rext2 = 0.878; F = 32.525) of P. falciparum. QSAR models identified the structural features of these favorable effects on antiplasmodial activities.

Molecular Descriptors Property Prediction Using Transformer-Based Approach

In this study, we introduce semi-supervised machine learning models designed to predict molecular properties. Our model employs a two-stage approach, involving pre-training and fine-tuning. Particularly, our model leverages a substantial amount of labeled and unlabeled data consisting of SMILES strings, a text representation system for molecules. During the pre-training stage, our model capitalizes on the Masked Language Model, which is widely used in natural language processing, for learning molecular chemical space representations. During the fine-tuning stage, our model is trained on a smaller labeled dataset to tackle specific downstream tasks, such as classification or regression. Preliminary results indicate that our model demonstrates comparable performance to state-of-the-art models on the chosen downstream tasks from MoleculeNet. Additionally, to reduce the computational overhead, we propose a new approach taking advantage of 3D compound structures for calculating the attention score used in the end-to-end transformer model to predict anti-malaria drug candidates. The results show that using the proposed attention score, our end-to-end model is able to have comparable performance with pre-trained models.

The truncated AaActin1 promoter is a candidate tool for metabolic engineering of artemisinin biosynthesis in Artemisia annua L

Malaria is a devastating parasitic disease with high levels of morbidity and mortality worldwide. Artemisinin, the active substance against malaria, is a sesquiterpenoid produced by Artemisia annua. To improve artemisinin content in the native A. annua plants, considerable efforts have been attempted, with genetic transformation serving as an effective strategy. Although, the most frequently-used cauliflower mosaic virus (CaMV) 35S (CaMV35S) promoter has proved to be efficient in A. annua transgenic studies, it appears to show weak activity in peltate glandular secretory trichomes (GSTs) of A. annua plants. Here, we characterized the 1727 bp fragment upstream from the translation start codon (ATG) of AaActin1, however, found it was inactive in tobacco. After removal of the 5' intron, the truncated AaActin1 promoter (tpACT) showed 69% and 50% activity of CaMV35S promoter in transiently transformed tobacco and stably transformed A. annua, respectively. β-glucuronidase (GUS) staining analysis showed that the tpACT promoter was capable of directing the constant expression of a foreign gene in peltate GSTs of transgenic A. annua, representing higher activity than CaMV35S promoter. Collectively, our study provided a novel promoter available for metabolic engineering of artemisinin biosynthesis in A. annua.