Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

Jaradat, Nour Jamal; Alshaer, Walhan; Hatmal, Ma’mon M.; Taha, Mutasem O.

doi:10.1039/d2ra07007c

Cited by 5 publications

(2 citation statements)

References 82 publications

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“…Therefore, the section corresponding to this protein in the machine learning training, or testing, tables included 10 row entries filled with the same ProtrWeb descriptors and differing 3D descriptors according to each crystallographic structure. The repetitive use of slightly differing data is commonly implemented in machine learning as data augmentation tool to enhance machine learning models 30 – 32 . All protein entries and descriptors are shown in supporting Excel file S M1 .…”

Section: Methodsmentioning

confidence: 99%

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Daoud,

Taha

2024

Sci Rep

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Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

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Section: Methodsmentioning

confidence: 99%

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Daoud,

Taha

2024

Sci Rep

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“…The molecular docking was executed using the Autodock Vina 1.1.2 software program to study the interaction of active curcuminoids with the STAT3 protein (PDB ID: 6NJS) [ 42 , 43 ]. This pdb has a high resolution of 2.70 Å and a co-crystallized ligand and is ideal for studying STAT3 inhibitors [ 44 ]. The 3D grid was designed around the SH2 domain containing interacting amino acids with a co-crystallized ligand.…”

Section: Methodsmentioning

confidence: 99%

Fluorinated and N-Acryloyl-Modified 3,5-Di[(E)-benzylidene]piperidin-4-one Curcuminoids for the Treatment of Pancreatic Carcinoma

et al. 2023

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Pancreatic carcinoma is a cancer disease with high mortality. Thus, new and efficient treatments for this disease are badly needed. Curcumin has previously shown promising effects in pancreatic cancer patients; however, this natural compound suffers from inadequate efficacy and bioavailability, preventing its clinical approval. The synthetic curcuminoid EF24 was developed with activities superior to curcumin against various cancer types. In this study, a series of analogs of EF24 were investigated for anticancer effects on pancreatic carcinoma models. A distinct activity boost was achieved by straightforward N-acrylation of EF24 analogs, in particular, of compounds bearing 3-fluoro-4-methoxybenzylidene, 3,4-difluorobenzylidene, and 4-trifluoromethylbenzylidene moieties, while no improvement was seen for N-acryloyl-modified EF24. Apoptosis induction and suppression of phospho-STAT3 levels were determined, the latter corroborated by docking of active curcuminoids into STAT3. Hence, promising new clues for the development of efficient and superior curcuminoids as valuable treatment options for one of the most lethal cancer diseases were discovered in this study.

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Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

Jaradat,

Hatmal,

Alqudah

et al. 2023

J Comput Aided Mol Des

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Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

Abstract: Novel STAT3 inhibitory lead of potent anti-STAT3 IC50 and novel chemotype was discovered using a data augmentation algorithm based on a computational sequence of docking, scoring, ligand-receptor contact fingerprints.

Cited by 5 publications

References 82 publications

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Fluorinated and N-Acryloyl-Modified 3,5-Di[(E)-benzylidene]piperidin-4-one Curcuminoids for the Treatment of Pancreatic Carcinoma

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

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