Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Kosugi, Takatsugu; Ohue, Masahito

doi:10.3390/ijms222010925

Cited by 21 publications

(14 citation statements)

References 43 publications

(52 reference statements)

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“…It is frequently used in the present small-molecule drug development process for computational approaches and to assess drug-like features. Most of the designed compounds showed an attractive range of QED [ 21 , 22 ]. Typically, the NP score falls somewhere in the range of −5 to 5.…”

Section: Resultsmentioning

confidence: 99%

Fighting Antibiotic Resistance: New Pyrimidine-Clubbed Benzimidazole Derivatives as Potential DHFR Inhibitors

Haque

Marathakam²,

Rana³

et al. 2023

Molecules

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The present work describes the design and development of seventeen pyrimidine-clubbed benzimidazole derivatives as potential dihydrofolate reductase (DHFR) inhibitors. These compounds were filtered by using ADMET, drug-likeness characteristics calculations, and molecular docking experiments. Compounds 27, 29, 30, 33, 37, 38, and 41 were chosen for the synthesis based on the results of the in silico screening. Each of the synthesized compounds was tested for its in vitro antibacterial and antifungal activities using a variety of strains. All the compounds showed antibacterial properties against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus pyogenes) as well as Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa). Most of the compounds either had a higher potency than chloramphenicol or an equivalent potency to ciprofloxacin. Compounds 29 and 33 were effective against all the bacterial and fungal strains. Finally, the 1,2,3,4-tetrahydropyrimidine-2-thiol derivatives with a 6-chloro-2-(chloromethyl)-1H-benzo[d]imidazole moiety are potent enough to be considered a promising lead for the discovery of an effective antibacterial agent.

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Section: Resultsmentioning

confidence: 99%

Fighting Antibiotic Resistance: New Pyrimidine-Clubbed Benzimidazole Derivatives as Potential DHFR Inhibitors

Haque

Marathakam²,

Rana³

et al. 2023

Molecules

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show abstract

“…The following parameters were analyzed when monitoring the pertinence of the inverse QSAR approach: Validity = #valid SMILES/#all generated text strings , which measures success to generate a syntactically valid SMILES string (assessed by CGRtools), starting from the input “high-affinity” ⟨ D⃗ ⟩ vectors. Feasibility assessing chemical feasibility and drug-likeness according to Ertl and QED indices. Novelty . A compound generated with ACoVAE is considered “novel” if it is not contained in the training database. …”

Section: Methodsmentioning

confidence: 99%

“…Feasibility assessing chemical feasibility and drug-likeness according to Ertl58 and QED59 indices. 3.…”

mentioning

confidence: 99%

Inverse QSAR: Reversing Descriptor-Driven Prediction Pipeline Using Attention-Based Conditional Variational Autoencoder

Bort

Mazitov

Horvath

et al. 2022

J. Chem. Inf. Model.

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In order to better foramize it, the notorious inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process including (i) finding "seed" descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the "seed" vectors. The main development effort here was focused on the latter stage, proposing a new attention-based conditional variational autoencoder neuralnetwork architecture based on recent developments in attentionbased methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as "orthogonal" in silico validation methods, proving that some of de novo structures are, beyond being predicted active by 2D-QSAR models, clearly able to match binding 3D pharmacophores and bind the protein pocket.

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“…2. Feasibility assessing chemical feasibility and drug-likeness according to the Ertl 58 and QED 59 indices.…”

Section: Solution Of Inverse Qsar Problem: the Acovae Algorithmmentioning

confidence: 99%

Inverse QSAR: reversing descriptor-driven prediction pipeline using attention-based conditional variational autoencoder (ACoVAE)

Bort

Mazitov

Horvath

et al. 2022

Preprint

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In order to better formalize the notorious Inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process1,2,3 including (i) finding “seed” descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the “seed” vectors. The main development effort here was focused on the latter stage, proposing a new Attention-based Conditional Variational AutoEncoder (ACoVAE) neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity, while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as “orthogonal” in silico validation methods, proving that some of de novo structures are, beyond being predicted active by 2D-QSAR models, clearly able to match binding 3D pharmacophores and bind the protein pocket

show abstract

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Cited by 21 publications

References 43 publications

Fighting Antibiotic Resistance: New Pyrimidine-Clubbed Benzimidazole Derivatives as Potential DHFR Inhibitors

Fighting Antibiotic Resistance: New Pyrimidine-Clubbed Benzimidazole Derivatives as Potential DHFR Inhibitors

Inverse QSAR: Reversing Descriptor-Driven Prediction Pipeline Using Attention-Based Conditional Variational Autoencoder

Inverse QSAR: reversing descriptor-driven prediction pipeline using attention-based conditional variational autoencoder (ACoVAE)

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