A fragment-based structural analysis of MMP-2 inhibitors in search of meaningful structural fragments

Banerjee, S.; Amin, Sk. Abdul; Jha, Tarun

doi:10.1016/j.compbiomed.2022.105360

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…More recently, through a combination of fragment‐based analysis, frequency‐based analysis and traditional machine learning techniques, important structures for MMP‐2 inhibitors were investigated by Banerjee et al. [22]. A notable difference in our study is the focus on vocabularies obtained through subword tokenization.…”

Section: Introductionmentioning

confidence: 99%

“…Yamanishi et al [21] have used sparse canonical correspondence analysis (SCCA) to identify a number of chemical substructures common among drugs with the ability to bind to a specific set of protein domains. More recently, through a combination of fragment-based analysis, frequency-based analysis and traditional machine learning techniques, important structures for MMP-2 inhibitors were investigated by Banerjee et al [22]. A notable difference in our study is the focus on vocabularies obtained through subword tokenization.…”

Section: Introductionmentioning

confidence: 99%

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Exploring data‐driven chemical SMILES tokenization approaches to identify key protein–ligand binding moieties

Temizer,

Uludoğan,

Özçelik

et al. 2024

Molecular Informatics

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring data‐driven chemical SMILES tokenization approaches to identify key protein–ligand binding moieties

Temizer,

Uludoğan,

Özçelik

et al. 2024

Molecular Informatics

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Heavily Labeled Signal Probe for Electrogenerated Chemiluminescence Peptide‐Based Biosensing of Matrix Metalloproteinase 2

Zhang,

Qian,

Yang

et al. 2024

Chemistry An Asian Journal

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The field of electrogenerated chemiluminescence (ECL) biosensing has witnessed remarkable growth, emphasizing the need for precise detection of biomarkers. The synthesis approach of peptide‐based signal probe with high recognition ability and high ECL efficiency is a significant issue in the ECL biosensing. Here, a heavily labeled signal probe was synthesized for ECL peptide‐based biosensing tactic by using a new aldehyde bearing cyclometalated Ir(III) complex ([Ir(bt)2(bpy‐CHO)PF6 (bt=2‐phenylbenzothiazole, bpy‐CHO=4’‐methyl‐[2,2’‐bipyridine]‐4‐carbaldehyde, denoted as Ir1) as ECL signal reagent and streptavidin (SA) as carrier protein. One ECL peptide‐based biosensing method was exemplified for the detection of matrix metalloproteinase 2 (MMP‐2) by using Ir1 labeled SA (SA−Ir1) as heavily labeled signal probe and biotinylated peptide as molecular recognition substrate. MMP‐2 was sensitively detected in the range from 5 to 100 ng/mL with a detection limit of 1.5 ng/mL. Importantly, two detection modes differing in the order of cleavage recognition by MMP‐2 and signal transduction with SA−Ir1 were compared for the first time. First cleavage and second signal transduction were proposed to be beneficial to sensitive detection of target, which provides some ideas for biomarker diagnostics in disease screening at an early stage.

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Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Sardar

Bhattacharya

Amin³

et al. 2023

Mol Divers

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A fragment-based structural analysis of MMP-2 inhibitors in search of meaningful structural fragments

Cited by 6 publications

References 36 publications

Exploring data‐driven chemical SMILES tokenization approaches to identify key protein–ligand binding moieties

Exploring data‐driven chemical SMILES tokenization approaches to identify key protein–ligand binding moieties

Heavily Labeled Signal Probe for Electrogenerated Chemiluminescence Peptide‐Based Biosensing of Matrix Metalloproteinase 2

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

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