Design and optimization of novel succinate dehydrogenase inhibitors against agricultural fungi based on Transformer model
Pian, C.; Zhang, Y.; Chai, J.; Li, L.; Zhao, W.; Zhang, L.; Chen, Y.; Xu, Z.; Yang, C.
Show abstract
Succinate dehydrogenase inhibitors (SDHIs) are a promising class of fungicides targeting the energy production pathway of pathogenic fungi. However, overuse has led to resistance, necessitating the development of new and effective SDHIs. This study takes the Transformer model to generate a customized virtual library of potential SDHIs. These candidates were then meticulously screened based on expert knowledge and synthetic feasibility, ultimately yielding several pyrazole carboxamide derivatives as the promising leads. Subsequent synthesis, antifungal activity testing, and structural optimization further refined these leads into potent SDHI candidates. This work marks the first application of a generative model to SDHI design, establishing a robust workflow for virtual library generation, screening, activity evaluation, and structure optimization. This provides one way for the rational design of future SDHIs, not only against fungi, but potentially other agricultural pathogens as well.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 97%
- Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors 97%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 97%
Similar papers in this journal
- PeruNPDB: The Peruvian Natural Products Database for in silico drug screening 97%
- Mechanistic insights into the Japanese Encephalitis Virus RNA dependent RNA polymerase protein inhibition by bioflavonoids from Azadirachta indica 97%
- Discovery of Z1362873773: A Novel Fascin Inhibitor from a Large Chemical Library for Colorectal Cancer 96%
Similar papers in this journal
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 97%
- Chalcogen derivatives for the treatment of African trypanosomiasis: biological evaluation of thio and seleno- semicarbazones and their azole derivatives 96%
- Support Vector Machine based prediction models for drug repurposing and designing novel drugs for colorectal cancer 96%
Similar papers in this journal
- Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6 96%
- DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Accurate Protein-Ligand Interaction Prediction 94%
- Chemical composition, antimicrobial and antioxidant activities of essential oils from the receptacle of sunflower (Helianthus annuus L.) 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.