Breast Cancer Multitarget Drug Candidate Computational Data

Computational data from a study proposing DdpMPyPEPhU as a novel multitarget drug candidate for breast cancer. The research generated 14,750 derivative compounds through molecular enumeration and evaluated binding affinities for three protein targets. The dataset includes docking scores, MM-GBSA energies, and results from molecular dynamics simulations.

Use Cases

• Analyze the binding affinities and docking scores for 14,750 derivative compounds against Glucocorticoid Receptor, ER-alpha, and CDK2 targets.
• Compare the Molecular Mechanics Generalised Born Surface Area (MM-GBSA) energies of candidate compounds with standard drugs Lapatinib and Tamoxifen.
• Validate structural stability using data from 250 ns molecular dynamics simulations under neutralised conditions.
• Study the pharmacokinetic properties and drug-like characteristics identified for the lead compound DdpMPyPEPhU.

Strengths

• Dataset is derived from a structured computational study generating 14,750 novel derivative compounds for analysis.
• Includes specific, quantitative results such as docking scores ranging from –14.869 to –4.57 kcal/mol and MM-GBSA energies from –72.32 to –11.97 kcal/mol.
• Validated through multiple computational methods including Density Functional Theory (DFT) optimisation and 5 ns WaterMap analysis.

Limitations

• Dataset size is small at 2.9 MB, indicating limited raw data volume for extensive machine learning training.
• Contains no primary clinical or patient data; all results are from in-silico computational modeling.
• The temporal coverage and update frequency are not specified, and the data reflects a single computational study.

Provenance

Source

figshare

Collection Method

Generated through in-silico computational methods including molecular docking, enumeration, DFT optimisation, and molecular dynamics simulations.

Time Range

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Freshness

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Geography

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