1H-Indole-2,3-dione 3-thiosemicarbazones carrying a 4-sulfamoylphenyl moiety with selective antiviral activity against reovirus-1.

1H-indole-2,3-dione 3-[4-(4-sulfamoylphenyl)thiosemicarbazones] (6a-j) were evaluated against Para-influenza-3, Reovirus-1, Sindbis, Coxsackie B4 and Punto Toro viruses. New 1-methyl-1H-indole-2,3-dione 3-[4-(4-sulfamoylphenyl)thiosemicarbazones] (7a-c) were synthesized to evaluate the contribution of methyl substitution at position 1- of the indole ring to antiviral activity. The test results showed that compounds 5-trifluoromethoxy- substituted 6c (EC50: 2-9 µM) and 5-bromo- substituted 6f (EC50: 2-3 µM) have non-toxic selective antiviral activity while not all standards are active against Reovirus-1. Molecular docking studies of 6c and 6f were carried out to determine the possible binding positions with Reovirus-1. Trifluoromethoxy and bromine substitutions at position 5- of the indole ring provided selective antiviral activity, while methyl substitution at position 1- of the indole ring significantly decreased the activity and increased toxicity against Reovirus-1.

Synthesis and molecular docking studies of 5-trifluoromethoxy-2-indolinones as cholinesterase dual inhibitors.

Background: In Alzheimer's disease, butyrylcholinesterase (BuChE) activity gradually increases, while acetylcholinesterase (AChE) activity decreases or remains unchanged. Dual inhibitors have important roles in regulation of synaptic acetylcholine levels and progression of Alzheimer's disease. Methods: 1-(Thiomorpholin-4-ylmethyl)/benzyl-5-trifluoromethoxy-2-indolinones (6-7) were synthesized. AChE and BuChE inhibitory effects were investigated with Ellman's method. Molecular docking studies were performed for analyzing the possible binding interactions at active sites. Results: Compound 6g was the strongest inhibitor against both AChE (Ki = 0.35 µM) and BuChE (Ki = 0.53 µM). It showed higher inhibitory effects than both donepezil and galantamine. Moreover, compound 7m had a higher inhibitory effect than galantamine and the effect was comparable to that of donepezil against both AChE (Ki = 0.69 µM) and BuChE (Ki = 0.95 µM). Conclusion: The benzyl substitution compared with 1-(thiomorpholin-4-ylmethyl) group significantly increased both AChE and BuChE inhibitory effects.

Exploring data-driven chemical SMILES tokenization approaches to identify key protein-ligand binding moieties.

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. To address this gap, we employ data-driven SMILES tokenization techniques such as Byte Pair Encoding, WordPiece, and Unigram to identify chemical words and compare the resulting vocabularies. To understand the chemical significance of these words, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. The experiments on multiple protein-ligand affinity datasets show that despite differences in words, lengths, and validity among the vocabularies generated by different subword tokenization algorithms, the identified key chemical words exhibit similarity. Further, we conduct case studies on a number of target to analyze the impact of key chemical words on binding. We find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our approach elucidates chemical properties of the words identified by machine learning models and can be used in drug discovery studies to determine significant chemical moieties.

Novel 2-indolinone derivatives as promising agents against respiratory syncytial and yellow fever viruses.

Background: A vaccine or antiviral drug for respiratory syncytial virus (RSV) infections and a specific antiviral drug for yellow fever virus (YFV) infections has not yet been developed. Method: In this study, 2-indolinone-based N-(4-sulfamoylphenyl)hydrazinecarbothioamides were synthesized. Along with these new compounds, previously synthesized 2-indolinone-based N-(3-sulfamoylphenyl)hydrazinecarbothioamides were evaluated against various DNA and RNA viruses. Results: Some 2-indolinone compounds exhibited nontoxic and selective antiviral activities against RSV and YFV. Halogen substitution at the indole ring increased the anti-RSV activities. Moreover, 1-benzyl and 5-halogen or nitro-substituted compounds were the most effective compounds against YFV. Conclusion: Generally, the 3-sulfonamide-substituted compounds were determined to be more effective than 4-sulfonamide-substituted compounds against RSV and YFV.