A computational protocol for the discovery of lead molecules targeting DNA unique to pathogens

Mishra, Aashwin; Sharma, Pradeep; Mrinal, Nirotpal; Jayaram, B.

doi:10.1016/j.ymeth.2017.07.017

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…HCV‐TARGET has a standardized, centralized medical record data abstraction method along with detailed data monitoring to increase the efficiency and quality of this observational registry. [ 9 ] Throughout treatment and during posttreatment follow‐up, sociodemographic, clinical, adverse‐event, and virologic data were collected. We complied with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines in reporting our work (Table ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning algorithms for predicting direct‐acting antiviral treatment failure in chronic hepatitis C: An HCV‐TARGET analysis

et al. 2022

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Background and Aims: We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection. Approach and Results: We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95%

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Section: Methodsmentioning

confidence: 99%

Machine learning algorithms for predicting direct‐acting antiviral treatment failure in chronic hepatitis C: An HCV‐TARGET analysis

et al. 2022

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Design and Characterization of Neutral Linker‐Based Bis‐Intercalator via Computer Simulations: Balancing DNA Binding and Cellular Uptake

Pant

2024

Chemistry & Biodiversity

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Bis‐intercalators play a significant role in altering the DNA structure, affecting its stability, and potentially influencing various cellular processes. These compounds have gained considerable attention in medicinal chemistry and biochemistry due to their potential applications in cancer therapy, where they may interfere with DNA replication and transcription, leading to anticancer effects. Traditionally, these molecules often possess a high positive charge to enhance their affinity for the negatively charged DNA. However, due to a high positive charge, their cellular uptake is compromised, along with their enhanced potential off‐target effects. In this study, we utilized bis‐intercalator TOTO and replaced the charged linker segment (propane‐1,3‐diammonium) with a neutral peroxodisulphuric acid linker. Using molecular modeling and computer simulations (500 ns, 3 replicas), we investigated the potential of the designed molecule as a bis‐intercalator and compared the properties with the control bis‐intercalator bound to DNA. We observed that the designed bis‐intercalator exhibited improved DNA binding (as assessed through MM‐PBSA and Delphi methods) and membrane translocation permeability. With an overall reduced charge, significantly less off‐target binding of the designed molecule is also anticipated. Consequently, bis‐intercalators based on peroxodisulphuric linkers can potentially target DNA effectively, and their role in the future design of bis‐intercalators is foreseen.

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A Rapid Computational Screening of Millions of Molecules to Identify Sequence-Specific DNA Minor Groove Binders via Physicochemical Descriptors

Pant

Jayaram

2021

Topics in Medicinal Chemistry

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A computational protocol for the discovery of lead molecules targeting DNA unique to pathogens

Cited by 4 publications

References 57 publications

Machine learning algorithms for predicting direct‐acting antiviral treatment failure in chronic hepatitis C: An HCV‐TARGET analysis

Machine learning algorithms for predicting direct‐acting antiviral treatment failure in chronic hepatitis C: An HCV‐TARGET analysis

Design and Characterization of Neutral Linker‐Based Bis‐Intercalator via Computer Simulations: Balancing DNA Binding and Cellular Uptake

A Rapid Computational Screening of Millions of Molecules to Identify Sequence-Specific DNA Minor Groove Binders via Physicochemical Descriptors

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